Photonic processing systems and methods

ABSTRACT

Aspects relate to a photonic processing system, a photonic processor, and a method of performing matrix-vector multiplication. An optical encoder may encode an input vector into a first plurality of optical signals. A photonic processor may receive the first plurality of optical signals; perform a plurality of operations on the first plurality of optical signals, the plurality of operations implementing a matrix multiplication of the input vector by a matrix; and output a second plurality of optical signals representing an output vector. An optical receiver may detect the second plurality of optical signals and output an electrical digital representation of the output vector.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation claiming the benefit under 35 U.S.C.§ 120 of U.S. application Ser. No. 16/986,655, entitled “PHOTONICPROCESSING SYSTEMS AND METHODS”, filed on Aug. 6, 2020 under AttorneyDocket No. L0858.70000US02, which is hereby incorporated herein byreference in its entirety.

Application Ser. No. 16/986,655 is a Continuation claiming the benefitunder 35 U.S.C. § 120 of U.S. application Ser. No. 16/412,098, entitled“PHOTONIC PROCESSING SYSTEMS AND METHODS”, filed on May 14, 2019 underAttorney Docket No. L0858.70000US01, which is hereby incorporated hereinby reference in its entirety.

Application Ser. No. 16/412,098 claims priority under 35 U.S.C. § 119(e)to U.S. Provisional Patent Application Ser. No. 62/671,793, entitled“ALGORITHMS FOR TRAINING NEURAL NETWORKS WITH PHOTONIC HARDWAREACCELERATORS,” filed on May 15, 2018 under Attorney Docket No.L0858.70001US00, which is hereby incorporated herein by reference in itsentirety.

Application Ser. No. 16/412,098 also claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No. 62/680,557,entitled “PHOTONICS PROCESSING SYSTEMS AND METHODS,” filed on Jun. 4,2018 under Attorney Docket No. L0858.70000US00, which is herebyincorporated herein by reference in its entirety.

Application Ser. No. 16/412,098 also claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No. 62/689,022,entitled “CONVOLUTIONAL LAYERS FOR NEURAL NETWORKS USING PROGRAMMABLENANOPHOTONICS,” filed on Jun. 22, 2018 under Attorney Docket No.L0858.70003US00, which is hereby incorporated herein by reference in itsentirety.

Application Ser. No. 16/412,098 also claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No. 62/755,402,entitled “REAL-NUMBER PHOTONIC ENCODING,” filed on Nov. 2, 2018 underAttorney Docket No. L0858.70008US00, which is hereby incorporated hereinby reference in its entirety.

Application Ser. No. 16/412,098 also claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No. 62/792,720,entitled “HIGH-EFFICIENCY DOUBLE-SLOT WAVEGUIDENANO-OPTOELECTROMECHANICAL PHASE MODULATOR,” filed on Jan. 15, 2019under Attorney Docket No. L0858.70006US00, which is hereby incorporatedherein by reference in its entirety.

Application Ser. No. 16/412,098 also claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No. 62/793,327,entitled “DIFFERENTIAL, LOW-NOISE HOMODYNE RECEIVER,” filed on Jan. 16,2019 under Attorney Docket No. L0858.70004US00, which is herebyincorporated herein by reference in its entirety.

Application Ser. No. 16/412,098 also claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No. 62/834,743entitled “STABILIZING LOCAL OSCILLATOR PHASES IN A PHOTOCORE,” filed onApr. 16, 2019 under Attorney Docket No. L0858.70014US00, which is herebyincorporated herein by reference in its entirety.

BACKGROUND

Conventional computation use processors that include circuits ofmillions of transistors to implement logical gates on bits ofinformation represented by electrical signals. The architectures ofconventional central processing units (CPUs) are designed for generalpurpose computing, but are not optimized for particular types ofalgorithms. Graphics processing, artificial intelligence, neuralnetworks, and deep learning are a few examples of the types ofalgorithms that are computationally intensive and are not efficientlyperformed using a CPU. Consequently, specialized processors have beendeveloped with architectures better-suited for particular algorithms.Graphical processing units (GPUs), for example, have a highly parallelarchitecture that makes them more efficient than CPUs for performingimage processing and graphical manipulations. After their developmentfor graphics processing, GPUs were also found to be more efficient thanGPUs for other memory-intensive algorithms, such as neural networks anddeep learning. This realization, and the increasing popularity ofartificial intelligence and deep learning, lead to further research intonew electrical circuit architectures that could further enhance thespeed of these algorithms.

SUMMARY

In some embodiments, a photonic processor is provided. The photonicprocessor may include a first array of interconnected variable beamsplitters (VBSs) comprising a first plurality of optical inputs and afirst plurality of optical outputs; a second array of interconnectedVBSs comprising a second plurality of optical inputs and a secondplurality of optical outputs; and a plurality of controllable opticalelements, each of the plurality of these controllable optical elementscoupling a single one of the first plurality of optical outputs of thefirst array to a respective single one of the second plurality ofoptical inputs of the second array.

In some embodiments, a photonic processing system is provided. Thephotonic processing system may include an optical encoder configured toencode an input vector into a first plurality of optical signals. Thephotonic processing system may also include a photonic processorconfigured to: receive the first plurality of optical signals, each ofthe first plurality of signals received by a respective input spatialmode of a plurality of input spatial modes of the photonic processor;perform a plurality of operations on the first plurality of opticalsignals, the plurality of operations implementing a matrixmultiplication of the input vector by a matrix; and output a secondplurality of optical signals representing an output vector, each of thesecond plurality of signals transmitted by a respective output spatialmode of a plurality of output spatial modes of the photonic processor.The photonic processing system may also include an optical receiverconfigured to detect the second plurality of optical signals and outputan electrical digital representation of the output vector.

In some embodiments, a method of optically performing matrix-vectormultiplication is provided. The method may include: receiving a digitalrepresentation of an input vector; encoding, using an optical encoder,the input vector into a first plurality of optical signals; performing,using a processor, a singular value decomposition (SVD) of a matrix todetermine a first, second, and third SVD matrix; controlling photonicprocessor comprising a plurality of variable beam splitters (VBS) tooptically implement the first, second, and third SVD matrix; propagatingthe first plurality of optical signals through the photonic processor;detecting a second plurality of optical signals received from thephotonic processor; and determining an output vector based on thedetected second plurality of optical signals, wherein the output vectorrepresents a result of the matrix-vector multiplication.

The foregoing apparatus and method embodiments may be implemented withany suitable combination of aspects, features, and acts described aboveor in further detail below. These and other aspects, embodiments, andfeatures of the present teachings can be more fully understood from thefollowing description in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects and embodiments of the application will be describedwith reference to the following figures. It should be appreciated thatthe figures are not necessarily drawn to scale. Items appearing inmultiple figures are indicated by the same reference number in all thefigures in which they appear.

FIG. 1-1 is a schematic diagram of a photonic processing system, inaccordance with some non-limiting embodiments.

FIG. 1-2 is a schematic diagram of an optical encoder, in accordancewith some non-limiting embodiments.

FIG. 1-3 is a schematic diagram of a photonic processor, in accordancewith some non-limiting embodiments.

FIG. 1-4 is a schematic diagram of an interconnected variable beamsplitter array, in accordance with some non-limiting embodiments.

FIG. 1-5 is a schematic diagram of a variable beam splitter, inaccordance with some non-limiting embodiments.

FIG. 1-6 is a schematic diagram of a diagonal attenuation and phaseshifting implementation, in accordance with some non-limitingembodiments.

FIG. 1-7 is a schematic diagram of an attenuator, in accordance withsome non-limiting embodiments.

FIG. 1-8 is a schematic diagram of a power tree, in accordance with somenon-limiting embodiments.

FIG. 1-9 is a schematic diagram of an optical receiver, in accordancewith some non-limiting embodiments.

FIG. 1-10 is a schematic diagram of a homodyne detector, in accordancewith some non-limiting embodiments.

FIG. 1-11 is a schematic diagram of a folded photonic processing system,in accordance with some non-limiting embodiments.

FIG. 1-12A is a schematic diagram of a wavelength-division-multiplexed(WDM) photonic processing system, in accordance with some non-limitingembodiments.

FIG. 1-12B is a schematic diagram of the frontend of thewavelength-division-multiplexed (WDM) photonic processing system of FIG.1-12A, in accordance with some non-limiting embodiments.

FIG. 1-12C is a schematic diagram of the backend of thewavelength-division-multiplexed (WDM) photonic processing system of FIG.1-12A, in accordance with some non-limiting embodiments.

FIG. 1-13 is a schematic diagram of a circuit for performing analogsummation of optical signals, in accordance with some non-limitingembodiments.

FIG. 1-14 is a schematic diagram of a photonic processing system withcolumn-global phases shown, in accordance with some non-limitingembodiments.

FIG. 1-15 is a plot showing the effects of uncorrected global phaseshifts on homodyne detection, in accordance with some non-limitingembodiments.

FIG. 1-16 is a plot showing the quadrature uncertainties of coherentstates of light, in accordance with some non-limiting embodiments.

FIG. 1-17 is an illustration of matrix multiplication, in accordancewith some non-limiting embodiments.

FIG. 1-18 is an illustration of performing matrix multiplication bysubdividing matrices into submatrices, in accordance with somenon-limiting embodiments.

FIG. 1-19 is a flowchart of a method of manufacturing a photonicprocessing system, in accordance with some non-limiting embodiments.

FIG. 1-20 is a flowchart of a method of manufacturing a photonicprocessor, in accordance with some non-limiting embodiments.

FIG. 1-21 is a flowchart of a method of performing an opticalcomputation, in accordance with some non-limiting embodiments.

FIG. 2-1 is a flow chart of a process for training a latent variablemodel, in accordance with some non-limiting embodiments.

FIG. 2-2 is a flow chart of a process for configuring a photonicsprocessing system to implement unitary transfer matrices, in accordancewith some non-limiting embodiments.

FIG. 2-3 is a flow chart of a process for computing an error vectorusing a photonics processing system, in accordance with somenon-limiting embodiments.

FIG. 2-4 is a flow chart of a process for determining updated parametersfor unitary transfer matrices, in accordance with some non-limitingembodiments.

FIG. 2-5 is a flow chart of a process for updating parameters forunitary transfer matrices, in accordance with some non-limitingembodiments.

FIG. 3-1 is a flowchart of a method for computing a forward pass througha convolutional layer, in accordance with some non-limiting embodiments.

FIG. 3-2 is a flowchart of a method for computing a forward pass througha convolutional layer, in accordance with some non-limiting embodiments.

FIG. 3-3A is a flowchart of a method suitable for computingtwo-dimensional convolutions, in accordance with some non-limitingembodiments.

FIG. 3-3B is a flowchart is a flowchart of a method suitable forbuilding a circulant matrix, in accordance with some non-limitingembodiments.

FIG. 3-4A illustrates a pre-processing step of building a filter matrixfrom input filter matrices including a plurality of output channels, inaccordance with some non-limiting embodiments.

FIG. 3-4B illustrates building a circulant matrix from input matricesincluding a plurality of input channels, in accordance with somenon-limiting embodiments.

FIG. 3-4C illustrates a two-dimensional matrix multiplication operation,in accordance with some non-limiting embodiments.

FIG. 3-4D illustrates a post-processing step of rotating vector rows, inaccordance with some non-limiting embodiments.

FIG. 3-4E illustrates a post-processing step of vector row addition, inaccordance with some non-limiting embodiments.

FIG. 3-4F illustrates reshaping an output matrix into multiple outputchannels, in accordance with some non-limiting embodiments.

FIG. 3-5 is a flowchart of a method for performing a one-dimensionalFourier transform, in accordance with some non-limiting embodiments.

FIG. 3-6 is a flowchart of a method for performing a two-dimensionalFourier transform, in accordance with some non-limiting embodiments.

FIG. 3-7 is a flowchart of a method for performing a two-dimensionalFourier transform, in accordance with some non-limiting embodiments.

FIG. 3-8 is a flowchart of a method for performing convolutions usingFourier transforms, in accordance with some non-limiting embodiments.

FIG. 4-1 is a block diagram illustrating an optical modulator, inaccordance with some non-limiting embodiments.

FIG. 4-2A is a block diagram illustrating an example of a photonicsystem, in accordance with some non-limiting embodiments.

FIG. 4-2B is a flowchart illustrating an example of a method forprocessing signed, real numbers in the optical domain, in accordancewith some non-limiting embodiments.

FIG. 4-3A is schematic diagram illustrating an example of a modulatorthat may be used in connection with the photonic system of FIG. 4-2A, inaccordance with some non-limiting embodiments.

FIG. 4-3B is a plot illustrating the intensity and phase spectralresponses of the modulator of FIG. 4-3A when no voltage is applied, inaccordance with some non-limiting embodiments.

FIG. 4-3C is a plot illustrating the intensity and phase spectralresponses of the modulator of FIG. 4-3A when driven at a certainvoltage, in accordance with some non-limiting embodiments.

FIG. 4-3D is an example of an encoding table associated with themodulator of FIG. 4-3A, in accordance with some non-limitingembodiments.

FIG. 4-3E is a visual representation, in the complex plane, of thespectral response of the modulator of FIG. 4-3A, in accordance with somenon-limiting embodiments.

FIGS. 4-4A though 4-4C are visual representations, in the complex plane,of different spectral responses at the output of an opticaltransformation unit, in accordance with some non-limiting embodiments.

FIG. 4-5 is a visual representation, in the complex plane, of howdetection of an optical signal may be performed, in accordance with somenon-limiting embodiments.

FIG. 4-6 is an example of a decoding table, in accordance with somenon-limiting embodiments.

FIG. 4-7 is a block diagram of an optical communication system, inaccordance with some non-limiting embodiments.

FIG. 4-8 is a plot illustrating an example of a power spectral densityoutput by the modulator of FIG. 4-7, in accordance with somenon-limiting embodiments.

FIG. 4-9 is a flowchart illustrating an example of a method forfabricating a photonic system, in accordance with some non-limitingembodiments.

FIG. 5-1 is a circuit diagram illustrating an example of a differentialoptical receiver, in accordance with some non-limiting embodiments.

FIG. 5-2 is a schematic diagram illustrating a photonic circuit that maybe coupled with the differential optical receiver of FIG. 5-1, inaccordance with some non-limiting embodiments.

FIG. 5-3A is a schematic diagram illustrating a substrate including aphotonic circuit, photodetectors and a differential operationalamplifier, in accordance with some non-limiting embodiments.

FIG. 5-3B is a schematic diagram illustrating a first substrateincluding a photonic circuit and photodetectors, and a second substrateincluding a differential operational amplifier, where the first andsecond substrates are flip-chip bonded to each other, in accordance withsome non-limiting embodiments.

FIG. 5-3C is a schematic diagram illustrating a first substrateincluding a photonic circuit and photodetectors, and a second substrateincluding a differential operational amplifier, where the first andsecond substrates are wire bonded to each other, in accordance with somenon-limiting embodiments.

FIG. 5-4 is a flowchart illustrating an example of a method forfabricating an optical receiver, in accordance with some non-limitingembodiments.

FIGS. 5-4A through 5-4F illustrate an example of a fabrication sequencefor an optical receiver, in accordance with some non-limitingembodiments.

FIG. 5-5 is a flowchart illustrating an example of a method forreceiving an optical signal, in accordance with some non-limitingembodiments.

FIG. 6-1A is a top view illustrating schematically aNano-Opto-Electromechanical Systems (NOEMS) phase modulator, inaccordance with some non-limiting embodiments.

FIG. 6-1B is a top view illustrating schematically a suspendedmulti-slot optical structure of the NOEMS phase modulator of FIG. 6-1A,in accordance with some non-limiting embodiments.

FIG. 6-1C is a plot illustrating an example of an optical mode arisingin the suspended multi-slot optical structure of FIG. 6-1B, inaccordance with some non-limiting embodiments.

FIG. 6-1D is a top view illustrating schematically a mechanicalstructure of the NOEMS phase modulator of FIG. 6-1A, in accordance withsome non-limiting embodiments.

FIG. 6-1E is a top view illustrating schematically a transition regionof the NOEMS phase modulator of FIG. 6-1A, in accordance with somenon-limiting embodiments.

FIG. 6-2 is a cross-sectional view of the NOEMS phase modulator of FIG.6-1A, taken in a yz-plane, and illustrating a suspended waveguide, inaccordance with some non-limiting embodiments.

FIG. 6-3 is a cross-sectional view of the NOEMS phase modulator of FIG.6-1A, taken in a xy-plane, and illustrating a portion of a suspendedmulti-slot optical structure, in accordance with some non-limitingembodiments.

FIGS. 6-4A through 6-4C are cross-sectional views illustrating how asuspended multi-slot optical structure can be mechanically driven tovary the widths of the slots between the waveguides, in accordance withsome non-limiting embodiments.

FIG. 6-5 is a plot illustrating how the effective index of a suspendedmulti-slot optical structure may vary as a function of the width of aslot, in accordance with some non-limiting embodiments.

FIG. 6-6 is a flowchart illustrating an example of a method forfabricating a NOEMS phase modulator, in accordance with somenon-limiting embodiments.

DETAILED DESCRIPTION

I. Photo-Core

A. Overview of Photonics-Based Processing

The inventors have recognized and appreciated that there are limitationsto the speed and efficiency of conventional processors based onelectrical circuits. Every wire and transistor in the circuits of anelectrical processor has a resistance, an inductance, and a capacitancethat cause propagation delay and power dissipation in any electricalsignal. For example, connecting multiple processor cores and/orconnecting a processor core to a memory uses a conductive trace with anon-zero impedance. Large values of impedance limit the maximum rate atwhich data can be transferred through the trace with a negligible biterror rate. In applications where time delay is crucial, such as highfrequency stock trading, even a delay of a few hundredths of a secondcan make an algorithm unfeasible for use. For processing that requiresbillions of operations by billions of transistors, these delays add upto a significant loss of time. In addition to electrical circuits'inefficiencies in speed, the heat generated by the dissipation of energycaused by the impedance of the circuits is also a barrier in developingelectrical processors.

The inventors further recognized and appreciated that using lightsignals, instead of electrical signals, overcomes many of theaforementioned problems with electrical computing. Light signals travelat the speed of light in the medium in which the light is traveling;thus the latency of photonic signals is far less of a limitation thanelectrical propagation delay. Additionally, no power is dissipated byincreasing the distance traveled by the light signals, opening up newtopologies and processor layouts that would not be feasible usingelectrical signals. Thus, light-based processors, such as aphotonics-based processor may have better speed and efficiencyperformance than conventional electrical processors.

Additionally, the inventors have recognized and appreciated that alight-based processor, such as a photonics-based processor, may bewell-suited for particular types of algorithms. For example, manymachine learning algorithms, e.g. support vector machines, artificialneural networks, probabilistic graphical model learning, rely heavily onlinear transformations on multi-dimensional arrays/tensors. The simplestexample is multiplying vectors by matrices, which using conventionalalgorithms has a complexity on the order of O(n²), where n is thedimensionality of the square matrices being multiplied. The inventorshave recognized and appreciated that a photonics-based processor, whichin some embodiment may be a highly parallel linear processor, canperform linear transformations, such as matrix multiplication, in ahighly parallel manner by propagating a particular set of input lightsignals through a configurable array of beam splitters. Using suchimplementations, matrix multiplication of matrices with dimension n=512can be completed in hundreds of picoseconds, as opposed to the tens tohundreds of nanoseconds using conventional processing. Using someembodiments, matrix multiplication is estimated to speed up by twoorders of magnitude relative to conventional techniques. For example, amultiplication that may be performed by a state-of-the-art graphicsprocessing unit (GPU) can be performed in about 10 ns can be performedby a photonic processing system according to some embodiments in about200 ps.

To implement a photonics-based processor, the inventors have recognizedand appreciated that the multiplication of an input vector by a matrixcan be accomplished by propagating coherent light signals, e.g., laserpulses, through a first array of interconnected variable beam splitters(VBSs), a second array of interconnected variable beam splitters, andmultiple controllable optical elements (e.g., electro-optical oroptomechanical elements) between the two arrays that connect a singleoutput of the first array to a single input of the second arrayDetailsof a photonic processing system that includes a photonic processor aredescribed below.

B. Photonic Processing System Overview

Referring to FIG. 1-1, a photonic processing system 1-100 includes anoptical encoder 1-101, a photonic processor 1-103, an optical receiver1-105, and a controller 1-107, according to some embodiments. Thephotonic processing system 1-100 receives, as an input from an externalprocessor (e.g., a CPU), an input vector represented by a group of inputbit strings and produces an output vector represented by a group ofoutput bit strings. For example, if the input vector is an n-dimensionalvector, the input vector may be represented by n separate bit strings,each bit string representing a respective component of the vector. Theinput bit string may be received as an electrical or optical signal fromthe external processor and the output bit string may be transmitted asan electrical or optical signal to the external processor. In someembodiments, the controller 1-107 does not necessarily output an outputbit string after every process iteration. Instead, the controller 1-107may use one or more output bit strings to determine a new input bitstream to feed through the components of the photonic processing system1-100. In some embodiments, the output bit string itself may be used asthe input bit string for a subsequent iteration of the processimplemented by the photonic processing system 1-100. In otherembodiments, multiple output bit streams are combined in various ways todetermine a subsequent input bit string. For example, one or more outputbit strings may be summed together as part of the determination of thesubsequent input bit string.

The optical encoder 1-101 is configured to convert the input bit stringsinto optically encoded information to be processed by the photonicprocessor 1-103. In some embodiments, each input bit string istransmitted to the optical encoder 1-101 by the controller 1-107 in theform of electrical signals. The optical encoder 1-101 converts eachcomponent of the input vector from its digital bit string into anoptical signal. In some embodiments, the optical signal represents thevalue and sign of the associated bit string as an amplitude and a phaseof an optical pulse. In some embodiments, the phase may be limited to abinary choice of either a zero phase shift or a it phase shift,representing a positive and negative value, respectively. Embodimentsare not limited to real input vector values. Complex vector componentsmay be represented by, for example, using more than two phase valueswhen encoding the optical signal. In some embodiments, the bit string isreceived by the optical encoder 1-101 as an optical signal (e.g., adigital optical signal) from the controller 1-107. In these embodiments,the optical encoder 1-101 converts the digital optical signal into ananalog optical signal of the type described above.

The optical encoder 1-101 outputs n separate optical pulses that aretransmitted to the photonic processor 1-103. Each output of the opticalencoder 1-101 is coupled one-to-one to a single input of the photonicprocessor 1-103. In some embodiments, the optical encoder 1-101 may bedisposed on the same substrate as the photonic processor 1-103 (e.g.,the optical encoder 1-101 and the photonic processor 1-103 are on thesame chip). In such embodiments, the optical signals may be transmittedfrom the optical encoder 1-101 to the photonic processor 1-103 inwaveguides, such as silicon photonic waveguides. In other embodiments,the optical encoder 1-101 may be disposed on a separate substrate fromthe photonic processor 1-103. In such embodiments, the optical signalsmay be transmitted from the optical encoder 1-101 to the photonicprocessor 103 in optical fiber.

The photonic processor 1-103 performs the multiplication of the inputvector by a matrix M. As described in detail below, the matrix M isdecomposed into three matrices using a combination of a singular valuedecomposition (SVD) and a unitary matrix decomposition. In someembodiments, the unitary matrix decomposition is performed withoperations similar to Givens rotations in QR decomposition. For example,an SVD in combination with a Householder decomposition may be used. Thedecomposition of the matrix M into three constituent parts may beperformed by the controller 1-107 and each of the constituent parts maybe implemented by a portion of the photonic processor 1-103. In someembodiments, the photonic processor 1-103 includes three parts: a firstarray of variable beam splitters (VBSs) configured to implement atransformation on the array of input optical pulses that is equivalentto a first matrix multiplication (see, e.g., the first matriximplementation 1-301 of FIG. 1-3); a group of controllable opticalelements configured to adjust the intensity and/or phase of each of theoptical pulses received from the first array, the adjustment beingequivalent to a second matrix multiplication by a diagonal matrix (see,e.g., the second matrix implementation 1-303 of FIG. 1-3); and a secondarray of VBSs configured to implement a transformation on the opticalpulses received from the group of controllable electro-optical element,the transformation being equivalent to a third matrix multiplication(see, e.g., the third matrix implementation 1-305 of FIG. 3).

The photonic processor 1-103 outputs n separate optical pulses that aretransmitted to the optical receiver 1-105. Each output of the photonicprocessor 1-103 is coupled one-to-one to a single input of the opticalreceiver 1-105. In some embodiments, the photonic processor 1-103 may bedisposed on the same substrate as the optical receiver 1-105 (e.g., thephotonic processor 1-103 and the optical receiver 1-105 are on the samechip). In such embodiments, the optical signals may be transmitted fromthe photonic processor 1-103 to the optical receiver 1-105 in siliconphotonic waveguides. In other embodiments, the photonic processor 1-103may be disposed on a separate substrate from the optical receiver 1-105.In such embodiments, the optical signals may be transmitted from thephotonic processor 103 to the optical receiver 1-105 in optical fibers.

The optical receiver 1-105 receives the n optical pulses from thephotonic processor 1-103. Each of the optical pulses is then convertedto electrical signals. In some embodiments, the intensity and phase ofeach of the optical pulses is measured by optical detectors within theoptical receiver. The electrical signals representing those measuredvalues are then output to the controller 1-107.

The controller 1-107 includes a memory 1-109 and a processor 1-111 forcontrolling the optical encoder 1-101, the photonic processor 1-103 andthe optical receiver 1-105. The memory 1-109 may be used to store inputand output bit strings and measurement results from the optical receiver1-105. The memory 1-109 also stores executable instructions that, whenexecuted by the processor 1-111, control the optical encoder 1-101,perform the matrix decomposition algorithm, control the VBSs of thephotonic processor 103, and control the optical receivers 1-105. Thememory 1-109 may also include executable instructions that cause theprocessor 1-111 to determine a new input vector to send to the opticalencoder based on a collection of one or more output vectors determinedby the measurement performed by the optical receiver 1-105. In this way,the controller 1-107 can control an iterative process by which an inputvector is multiplied by multiple matrices by adjusting the settings ofthe photonic processor 1-103 and feeding detection information from theoptical receiver 1-105 back to the optical encoder 1-101. Thus, theoutput vector transmitted by the photonic processing system 1-100 to theexternal processor may be the result of multiple matrix multiplications,not simply a single matrix multiplication.

In some embodiments, a matrix may be too large to be encoded in thephotonic processor using a single pass. In such situations, one portionof the large matrix may be encoded in the photonic processor and themultiplication process may be performed for that single portion of thelarge matrix. The results of that first operation may be stored inmemory 1-109. Subsequently, a second portion of the large matrix may beencoded in the photonic processor and a second multiplication processmay be performed. This “chunking” of the large matrix may continue untilthe multiplication process has been performed on all portions of thelarge matrix. The results of the multiple multiplication processes,which may be stored in memory 1-109, may then be combined to form thefinal result of the multiplication of the input vector by the largematrix.

In other embodiments, only collective behavior of the output vectors isused by the external processor. In such embodiments, only the collectiveresult, such as the average or the maximum/minimum of multiple outputvectors, is transmitted to the external processor.

C. Optical Encoder

Referring to FIG. 1-2, the optical encoder includes at least one lightsource 1-201, a power tree 1-203, an amplitude modulator 1-205, a phasemodulator 1-207, a digital to analog converter (DAC) 1-209 associatedwith the amplitude modulator 1-205, and a 1-DAC 211 associated with thephase modulator 1-207, according to some embodiments. While theamplitude modulator 1-205 and phase modulator 1-207 are illustrated inFIG. 1-2 as single blocks with n inputs and n outputs (each of theinputs and outputs being, for example, a waveguide), in some embodimentseach waveguide may include a respective amplitude modulator and arespective phase modulator such that the optical encoder includes namplitude modulators and n phase modulators. Moreover, there may be anindividual DAC for each amplitude and phase modulator. In someembodiments, rather than having an amplitude modulator and a separatephase modulator associated with each waveguide, a single modulator maybe used to encode both amplitude and phase information. While using asingle modulator to perform such an encoding limits the ability toprecisely tune both the amplitude and phase of each optical pulse, thereare some encoding schemes that do not require precise tuning of both theamplitude and phase of the optical pulses. Such a scheme is describedlater herein.

The light source 1-201 may be any suitable source of coherent light. Insome embodiments, the light source 1-201 may be a diode laser or avertical-cavity surface emitting lasers (VCSEL). In some embodiments,the light source 1-201 is configured to have an output power greaterthan 10 mW, greater than 25 mW, greater than 50 mW, or greater than 75mW. In some embodiments, the light source 1-201 is configured to have anoutput power less than 100 mW. The light source 1-201 may be configuredto emit a continuous wave of light or pulses of light (“optical pulses”)at one or more wavelengths (e.g., the C-band or O-band). The temporalduration of the optical pulses may be, for example, about 100 ps.

While light source 1-201 is illustrated in FIG. 1-2 as being on the samesemiconductor substrate as the other components of the optical encoder,embodiments are not so limited. For example, the light source 1-201 maybe a separate laser packaging that is edge-bonded or surface-bonded tothe optical encoder chip. Alternatively, the light source 1-201 may becompletely off-chip and the optical pulses may be coupled to a waveguide1-202 of the optical encoder 1-101 via an optical fiber and/or a gratingcoupler.

The light source 1-201 is illustrated as two light sources 1-201 a and1-201 b, but embodiments are not so limited. Some embodiments mayinclude a single light source. Including multiple light sources 201 a-b,which may include more than two light sources, can provide redundancy incase one of the light sources fails. Including multiple light sourcesmay extend the useful lifetime of the photonic processing system 1-100.The multiple light sources 1-201 a-b may each be coupled to a waveguideof the optical encoder 1-101 and then combined at a waveguide combinerthat is configured to direct optical pulses from each light source tothe power tree 1-203. In such embodiments, only one light source is usedat any given time.

Some embodiments may use two or more phase-locked light sources of thesame wavelength at the same time to increase the optical power enteringthe optical encoder system. A small portion of light from each of thetwo or more light sources (e.g., acquired via a waveguide tap) may bedirected to a homodyne detector, where a beat error signal may bemeasured. The bear error signal may be used to determine possible phasedrifts between the two light sources. The beat error signal may, forexample, be fed into a feedback circuit that controls a phase modulatorthat phase locks the output of one light source to the phase of theother light source. The phase-locking can be generalized in amaster-slave scheme, where N≥1 slave light sources are phase-locked to asingle master light source. The result is a total of N+1 phase-lockedlight sources available to the optical encoder system.

In other embodiments, each separate light source may be associated withlight of different wavelengths. Using multiple wavelengths of lightallows some embodiments to be multiplexed such that multiplecalculations may be performed simultaneously using the same opticalhardware.

The power tree 1-203 is configured to divide a single optical pulse fromthe light source 1-201 into an array of spatially separated opticalpulses. Thus, the power tree 1-203 has one optical input and n opticaloutputs. In some embodiments, the optical power from the light source1-201 is split evenly across n optical modes associated with nwaveguides. In some embodiments, the power tree 1-203 is an array of50:50 beam splitters 1-801, as illustrated in FIG. 1-8. The number“depth” of the power tree 1-203 depends on the number of waveguides atthe output. For a power tree with n output modes, the depth of the powertree 1-203 is ceil(log₂(n)). The power tree 1-203 of FIG. 1-8 onlyillustrates a tree depth of three (each layer of the tree is labeledacross the bottom of the power tree 1-203). Each layer includes 2^(m-1)beam splitters, where m is the layer number. Consequently, the firstlayer has a single beam splitter 1-801 a, the second layer has two beamsplitters 1-801 b-1-801 c, and the third layer has four beam splitters1-801 d-1-801 g.

While the power tree 1-203 is illustrated as an array of cascading beamsplitters, which may be implemented as evanescent waveguide couplers,embodiments are not so limited as any optical device that converts oneoptical pulse into a plurality of spatially separated optical pulses maybe used. For example, the power tree 1-203 may be implemented using oneor more multimode interferometers (MMI), in which case the equationsgoverning layer width and depth would be modified appropriately.

No matter what type of power tree 1-203 is used, it is likely thatmanufacturing a power tree 1-203 such that the splitting ratios areprecisely even between the n output modes will be difficult, if notimpossible. Accordingly, adjustments can be made to the setting of theamplitude modulators to correct for the unequal intensities of the noptical pulses output by the power tree. For example, the waveguide withthe lowest optical power can be set as the maximum power for any givenpulse transmitted to the photonic processor 1-103. Thus, any opticalpulse with a power higher than the maximum power may be modulated tohave a lower power by the amplitude modulator 1-205, in addition to themodulation to the amplitude being made to encode information into theoptical pulse. A phase modulator may also be placed at each of the noutput modes, which may be used to adjust the phase of each output modeof the power tree 1-203 such that all of the output signals have thesame phase.

Alternatively or additionally, the power tree 1-203 may be implementedusing one or more Mach-Zehnder Interferometers (MZI) that may be tunedsuch that the splitting ratios of each beam splitter in the power treeresults in substantially equal intensity pulses at the output of thepower tree 1-203.

The amplitude modulator 1-205 is configured to modify, based on arespective input bit string, the amplitude of each optical pulsereceived from the power tree 1-203. The amplitude modulator 1-205 may bea variable attenuator or any other suitable amplitude modulatorcontrolled by the DAC 1-209, which may further be controlled by thecontroller 1-107. Some amplitude modulators are known fortelecommunication applications and may be used in some embodiments. Insome embodiments, a variable beam splitter may be used as an amplitudemodulator 1-205, where only one output of the variable beam splitter iskept and the other output is discarded or ignored. Other examples ofamplitude modulators that may be used in some embodiments includetraveling wave modulators, cavity-based modulators, Franz-Keldyshmodulators, plasmon-based modulators, 2-D material-based modulators andnano-opto-electromechanical switches (NOEMS).

The phase modulator 1-207 is configured to modify, based on therespective input bit string, the phase of each optical pulse receivedfrom the power tree 1-203. The phase modulator may be a thermo-opticphase shifter or any other suitable phase shifter that may beelectrically controlled by the 1-211, which may further be controlled bythe controller 1-107.

While FIG. 1-2 illustrates the amplitude modulator 1-205 and phasemodulator 1-207 as two separate components, they may be combined into asingle element that controls both the amplitudes and phases of theoptical pulses. However, there are advantages to separately controllingthe amplitude and phase of the optical pulse. Namely, due to theconnection between amplitude shifts and phase shifts via theKramers-Kronenig relations, there is a phase shift associated with anyamplitude shift. To precisely control the phase of an optical pulse, thephase shift created by the amplitude modulator 1-205 should becompensated for using the phase modulator 1-207. By way of example, thetotal amplitude of an optical pulse exiting the optical encoder 1-101 isA=a₀a₁a₂ and the total phase of the optical pulse exiting the opticalencoder is θ=Δθ+Δφ+φ, where a₀ is the input intensity of the inputoptical pulse (with an assumption of zero phase at the input of themodulators), a₁ is the amplitude attenuation of the amplitude modulator1-205, Δθ is the phase shift imparted by the amplitude modulator 1-205while modulating the amplitude, Δφ is the phase shift imparted by thephase modulator 1-207, a₂ is the attenuation associated with the opticalpulse passing through the phase modulator 1-209, and φ is the phaseimparted on the optical signal due to propagation of the light signal.Thus, setting the amplitude and the phase of an optical pulse is not twoindependent determinations. Rather, to accurately encode a particularamplitude and phase into an optical pulse output from the opticalencoder 1-101, the settings of both the amplitude modulator 1-205 andthe phase modulator 1-207 should be taken into account for bothsettings.

In some embodiments, the amplitude of an optical pulse is directlyrelated to the bit string value. For example, a high amplitude pulsecorresponds to a high bit string value and a low amplitude pulsecorresponds to a low bit string value. The phase of an optical pulseencodes whether the bit string value is positive or negative. In someembodiments, the phase of an optical pulse output by the optical encoder1-101 may be selected from two phases that are 180 degrees (π radians)apart. For example, positive bit string values may be encoded with azero degree phase shift and negative bit string values may be encodedwith a 180 degree (π radians) phase shift. In some embodiments, thevector is intended to be complex-valued and thus the phase of theoptical pulse is chosen from more than just two values between 0 and 2π.

In some embodiments, the controller 1-107 determines the amplitude andphase to be applied by both the amplitude modulator 1-205 and the phasemodulator 1-207 based on the input bit string and the equations abovelinking the output amplitude and output phase to the amplitudes andphases imparted by the amplitude modulator 1-204 and the phase modulator1-207. In some embodiments, the controller 1-107 may store in memory1-109 a table of digital values for driving the amplitude modulator1-205 and the phase modulator 1-207. In some embodiments, the memory maybe placed in close proximity to the modulators to reduce thecommunication temporal latency and power consumption.

The digital to analog converter (DAC) 1-209, associated with andcommunicatively coupled to the amplitude modulator 1-205, receives thedigital driving value from the controller 1-107 and converts the digitaldriving value to an analog voltage that drives the amplitude modulator1-205. Similarly, the DAC 1-211, associated with and communicativelycoupled to the phase modulator 1-207, receives the digital driving valuefrom the controller 1-107 and converts the digital driving value to ananalog voltage that drives the phase modulator 1-207. In someembodiments, the DAC may include an amplifier that amplifies the analogvoltages to sufficiently high levels to achieve the desired extinctionratio within the amplitude modulators (e.g., the highest extinctionratio physically possible to implement using the particular phasemodulator) and the desired phase shift range within the phase modulators(e.g., a phase shift range that covers the full range between 0 and 2π).While the DAC 1-209 and the DAC 1-211 are illustrated in FIG. 1-2 asbeing located in and/or on the chip of the optical encoder 1-101, insome embodiments, the DACs 1-209 and 1-211 may be located off-chip whilestill being communicatively coupled to the amplitude modulator 1-205 andthe phase modulator 1-207, respectively, with electrically conductivetraces and/or wires.

After modulation by the amplitude modulator 1-205 and the phasemodulator 1-207, the n optical pulses are transmitted from the opticalencoder 1-101 to the photonic processor 1-103.

D. Photonic Processor

Referring to FIG. 1-3, the photonic processor 1-103 implements matrixmultiplication on an input vector represented by the n input opticalpulse and includes three main components: a first matrix implementation1-301, a second matrix implementation 1-303, and a third matriximplementation 1-305. In some embodiments, as discussed in more detailbelow, the first matrix implementation 1-301 and the third matriximplementation 1-305 include an interconnected array of programmable,reconfigurable, variable beam splitters (VBSs) configured to transformthe n input optical pulses from an input vector to an output vector, thecomponents of the vectors being represented by the amplitude and phaseof each of the optical pulses. In some embodiments, the second matriximplementation 1-303 includes a group of electro-optic elements.

The matrix by which the input vector is multiplied, by passing the inputoptical pulses through the photonic processor 1-103, is referred to asM. The matrix M is a general m×n known to the controller 1-107 as thematrix that should be implemented by the photonic processor 1-103. Assuch, the controller 1-107 decomposes the matrix M using a singularvalue decomposition (SVD) such that the matrix M is represented by threeconstituent matrices: M=V^(T)ΣU, where U and V are real orthogonal n×nand m×m matrices, respectively (U^(T)U=UU^(T)=I and V^(T)V=VV^(T)=I),and Σ is an m×n diagonal matrix with real entries. The superscript “T”in all equations represents the transpose of the associated matrix.Determining the SVD of a matrix is known and the controller 1-107 mayuse any suitable technique to determine the SVD of the matrix M. In someembodiments, the matrix M is a complex matrix, in which case the matrixM can be decomposed into M=V^(†)ΣU, where V and U are complex unitaryn×n and m×m matrices, respectively U^(†)U=UU^(†)=I and V^(†)V=VV^(†)=I),and Σ is an m×n diagonal matrix with real or complex entries. The valuesof the diagonal singular values may also be further normalized such thatthe maximum absolute value of the singular values is 1.

Once the controller 1-107 has determined the matrices U, Σ and V for thematrix M, in the case where the matrices U and V are orthogonal realmatrices, the control may further decompose the two orthogonal matricesU and V into a series of real-valued Givens rotation matrices. A Givensrotation matrix G (i, j, θ) is defined component-wise by the followingequations:

g _(kk)=1 for k≠i,j

g _(kk)=cos(θ) for k=i,j

g _(ij) =−g _(ji)=−sin(θ),

g _(kl)=0 otherwise,

where g_(ij) represents the element in the i-th row and j-th column ofthe matrix G and θ is the angle of rotation associated with the matrix.Generally, the matrix G is an arbitrary 2×2 unitary matrix withdeterminant 1 (SU(2) group) and it is parameterized by two parameters.In some embodiments, those two parameters are the rotation angle θ andanother phase value φ. Nevertheless, the matrix G can be parameterizedby other values other than angles or phases, e.g. byreflectivities/transmissivities or by separation distances (in the caseof NOEMS).

Algorithms for expressing an arbitrary real orthogonal matrix in termsof a product of sets of Givens rotations in the complex space areprovided in M. Reck, et al., “Experimental realization of any discreteunitary operator,” Physical Review Letters 73, 58 (1994) (“Reck”), andW. R. Clements, et al., “Optimal design for universal multiportinterferometers,” Optica 3, 12 (2016) (“Clements”), both of which areincorporated herein by reference in their entirety and at least fortheir discussions of techniques for decomposing a real orthogonal matrixin terms of Givens rotations. (In the case that any terminology usedherein conflicts with the usage of that terminology in Reck and/orClements, the terminology should be afforded a meaning most consistentwith how a person of ordinary skill would understand its usage herein.).The resulting decomposition is given by the following equation:

${U = {D{\prod\limits_{k = 1}^{n}{\prod\limits_{{({i,j})} \in S_{k}}{G\left( {i,j,\theta_{ij}^{(k)}} \right)}}}}},$

where U is an n×n orthogonal matrix, S_(k) is the set of indicesrelevant to the k-th set of Givens rotations applied (as defined by thedecomposition algorithm), θ_(ij) ^((k)) represents the angle applied forthe Givens rotation between components i and j in the k-th set of Givensrotations, and D is a diagonal matrix of either +1 or −1 entriesrepresenting global signs on each component. The set of indices S_(k) isdependent on whether n is even or odd. For example, when n is even:

-   -   S_(k)={(1,2), (3,4), . . . , (n−1, n)} for odd k    -   S_(k)={(2,3), (4,5), . . . , (n−2, n−1)} for even k

When n is odd:

-   -   S_(k)={(1,2), (3,4), . . . , (n−2, n−1)} for odd k    -   S_(k)={(2,3), (4,5), . . . , (n−1, n)} for even k

By way of example and not limitation, the decomposition of a 4×4orthogonal matrix can be represented as:

U=DG(1,2,θ₁₂ ⁽¹⁾)G(3,4,θ₃₄ ⁽¹⁾)G(2,3,θ₂₃ ⁽²⁾)G(1,2,θ₁₂ ⁽³⁾)G(3,4,θ₃₄⁽³⁾)G(2,3,θ₂₃ ⁽⁴⁾)

A brief overview of one embodiment of an algorithm for decomposing ann×n matrix U in terms of n sets of real-valued Givens rotations, whichmay be implemented using the controller 1-107, is as follows:

  U^(′) ← U  For  i  from  1  to  u − 1:  If  i  is  odd:❘  For  j = 0  to  i − 1 : Find  G_(i − j, i − j + 1)^(T)(θ)  that  nullifies  element  ?  i.e.  θ = tan⁻¹(?/?)  U^(′) ← U^(′)G_(i − j, i − j + 1)^(T)(θ)  Else  if  i  is  even:  For  j = 1  to  i : Find  ?(θ)  that  nullifies  element  ?  i.e.  θ = tan⁻¹(−?/?)  U^(′) ← ?(θ)U^(′) ?indicates text missing or illegible when filed

The resultant matrix U′ of the above algorithm is lower triangular andis related to the original matrix U by the equation:

${U = {{\prod\limits_{{({j,k})} \in S_{L}}{G_{jk}^{T}U^{\prime}{\prod\limits_{{({j,k})} \in S_{k}}G_{jk}}}} = {D_{U}{\prod\limits_{{({j,k})} \in S}G_{jk}}}}},$

where the label S_(L) labels the set of two modes connected by the VBSto the left of U′ and the label S_(R) labels the set of two modesconnected by the VBS to the right of U′. Because U is an orthogonalmatrix, U′ is a diagonal matrix with {−1,1} entries along the diagonal.This matrix, U′=D_(U), is referred to as a “phase screen.”

The next step of the algorithm, is to repeatedly find which G^(T)_(jk)(θ₁)D_(U)=D_(U)G_(jk)(θ₂) is accomplished using the followingalgorithm, which may be implemented using the controller 1-107:

  For every ( 

 )in S 

  :  If U 

 and U 

 have different signs:   θ₂ =− θ₁  Else:   θ₂ = θ 

 

indicates data missing or illegible when filed

The above algorithm may also be used to decompose V and/or V^(T) todetermine the m layers of VBS values and the associated phase screen.

The above concept of decomposing an orthogonal matrix into real-valuedGivens rotation matrices can be expanded to complex matrices, e.g.,unitary matrices rather than orthogonal matrices. In some embodiments,this may be accomplished by including an additional phase in theparameterization of the Givens rotation matrices. Thus, a general formof the Givens matrices with the addition of the additional phase term isT(i,j,θ,ϕ), where

t _(kk)=1 for k≠i,j,

t _(ii) =e ^(iϕ)cos(θ),

t _(jj)=cos(θ),

t _(ij)=−sin(θ),

t _(ji) =e ^(iϕ)sin(θ),

t _(kl)=0 otherwise,

where t_(ij) represents the i-th row and j-th column of the matrix T, θis the angle of rotation associated with the matrix, and ϕ is theadditional phase. Any unitary matrix can be decomposed into matrices ofthe type T(i,j,θ,ϕ). By making the choice to set the phase ϕ=0, theconventional real-valued Givens rotation matrices described above areobtained. If, instead, the phase ϕ=π, then a set of matrices known asHouseholder matrices are obtained. A Householder matrix, H, has the formH=I−(ν⊗ν), where I is the n×n identity matrix, ν is a unit vector, and ⊗is the outer product. Householder matrices represent reflections about ahyperplane orthogonal to the unit vector v. In this parameterization thehyperplane is a two-dimensional subspace, rather than an n−1 dimensionalsubspace as is common in defining Householder matrices for the QRdecomposition. Thus, a decomposition of a matrix into Givens rotationsis equivalent to a decomposition of the matrix into Householdermatrices.

Based on the aforementioned decomposition of an arbitrary unitary matrixinto a restricted set of Givens rotations, any unitary matrix can beimplemented by a particular sequence of rotations and phase shifts. Andin photonics, rotations may be represented by variable beam splitters(VBS) and phase shifts are readily implemented using phase modulators.Accordingly, for the n optical inputs of the photonic processor 1-103,the first matrix implementation 1-301 and the third matriximplementation 1-305, representing the unitary matrices of the SVD ofthe matrix M may be implemented by an interconnected array of VBSs andphase shifters. Because of the parallel nature of passing optical pulsesthrough a VBS array, matrix multiplication can be performed in O(1)time. The second matrix implementation 1-303 is a diagonal matrix of theSVD of the matrix M combined with the diagonal matrices D associatedwith each of the orthogonal matrices of the SVD. As mentioned above,each matrix D is referred to as a “phase screen” and can be labeled witha subscript to denote whether it is the phase screen associated with thematrix U or the matrix V. Thus, the second matrix implementation 303 isthe matrix Σ′=D_(V)ΣD_(U).

In some embodiments, the VBS unit cell of the photonic processor 1-103associated with the first matrix implementation 1-301 and the thirdmatrix implementation 1-305 may be a Mach-Zehnder interferometer (MZI)with an internal phase shifter. In other embodiments, the VBS unit cellmay be a microelectromechanical systems (MEMS) actuator. An externalphase shifter may be used in some embodiments to implement theadditional phase needed for the Givens rotations.

The second matrix implementation 1-303, representing the diagonal matrixD_(V)ΣD_(U) may be implemented using an amplitude modulator and a phaseshifter. In some embodiments, a VBS may be used to split off a portionof light that can be dumped to variably attenuate an optical pulse.Additionally or alternatively, a controllable gain medium may be used toamplify an optical signal. For example, GaAs, InGaAs, GaN, or InP may beused as an active gain medium for amplifying an optical signal. Otheractive gain processes such as the second harmonic generation inmaterials with crystal inversion symmetric, e.g. KTP and lithiumniobate, and the four-wave mixing processes in materials that lackinversion symmetry, e.g. silicon, can also be used. A phase shifter ineach optical mode may be used to apply either a zero or a π phase shift,depending on the phase screen being implemented. In some embodiments,only a single phase shifter for each optical mode is used rather thanone phase shifter for each phase screen. This is possible because eachof the matrices D_(V), Σ, and D_(U) are diagonal and therefore commute.Thus, the value of each phase shifter of the second matriximplementation 1-303 of the photonic processor 1-103 is the result ofthe product of the two phase screens: D_(V)D_(U).

Referring to FIG. 1-4, the first and third matrix implementation 1-301and 1-305 are implemented as an array of VBSs 1-401, according to someembodiments. For the sake of simplicity, only n=6 input optical pulses(the number of rows) are illustrated resulting in a “circuit depth”(e.g., the number of columns) equal to the number of input opticalpulses (e.g., six). For the sake of clarity, only a single VBS 1-401 islabeled with a reference numeral. The VBS are labeled, however, withsubscripts that identify which optical modes are being mixed by aparticular VBS and a super script labeling the associated column. EachVBS 1-401 implements a complex Givens rotation, T(i,j,θ,ϕ), as discussedabove, where i and j are equivalent to the subscript labels of the VBSs1-401, θ is the rotation angle of the Givens rotation, and ϕ is theadditional phase associated with the generalized rotation.

Referring to FIG. 1-5, each VBS 1-401 may be implemented using a MZI1-510 and at least one external phase shifter 1-507. In someembodiments, a second external phase shifter 1-509 may also be included.The MZI 1-510 includes a first evanescent coupler 1-501 and a secondevanescent coupler 1-503 for mixing the two input modes of the MZI1-510. An internal phase shifter 1-505 modulates the phase θ in one armof the MZI 1-510 to create a phase difference between the two arms.Adjusting the phase θ causes the intensity of light output by the VBS1-401 to vary from one output mode of the MZI 1-510 to the other therebycreating a beam splitter that is controllable and variable. In someembodiments, a second internal phase shifter can be applied on thesecond arm. In this case, it is the difference between the two internalphase shifters that cause the output light intensity to vary. Theaverage between the two internal phases will impart a global phase tothe light that enter mode i and mode j. Thus the two parameters θ and ϕmay each be controlled by a phase shifter. In some embodiments, thesecond external phase shifter 1-509 may be used to correct for anunwanted differential phase across the output modes of the VBS due tostatic phase disorder.

In some embodiments, the phase shifters 1-505, 1-507 and 1-509 mayinclude a thermo-optic, electro-optic, or optomechanic phase modulator.In other embodiments, rather than including an internal phase modulator505 within an MZI 510, a NOEMS modulator may be used.

In some embodiments, the number of VBSs grows with the size of thematrix. The inventors have recognized and appreciated that controlling alarge number of VBSs can be challenging and there is a benefit tosharing a single control circuit among multiple VBSs. An example of aparallel control circuit that may be used to control multiple VBSs is adigital-to-analog converter receives as an input a digital string thatencodes the analog signal to be imparted on a specific VBS. In someembodiments, the circuit also receives a second input the address of theVBS that is to be controlled. The circuit may then impart analog signalson the addressed VBS. In other embodiments, the control circuit mayautomatically scan through a number of VBSs and impart analog signals onthe multiple VBSs without being actively given an address. In this case,the addressing sequence is predefined such that it traverses the VBSarray in known order.

Referring to FIG. 1-6, the second matrix implementation 1-303 implementsmultiplication by the diagonal matrix Σ′=D_(V)ΣD_(U). This may beaccomplished using two phase shifters 1-601 and 1-605 to implement thetwo phase screens and an amplitude modulator 1-603 to adjust theintensity of an associate optical pulse by an amount η. As mentionedabove, in some embodiments only a single phase modulator 1-601 may beused, as the two phase screens can be combined together since the threeconstituent matrices that form Σ′ are diagonal and therefore commute.

In some embodiments, the amplitude modulators 1-603 may be implementedusing an attenuator and/or an amplifier. If the value of the amplitudemodulation η is greater than one, the optical pulse is amplified. If thevalue of the amplitude modulation η is less than one, the optical pulseis attenuated. In some embodiments, only attenuation is used. In someembodiments, the attenuation may be implemented by a column ofintegrated attenuators. In other embodiments, as illustrated in FIG.1-7, the attenuation 1-603 may be implemented using a MZI that includestwo evanescent couplers 1-701 and 1-703 and a controllable internalphase shifter 1-705 to adjust how much of the input light is transmittedfrom the input of the MZI to a first output port 1-709 of the MZI. Asecond output port 1-707 of the MZI may be ignored, blocked or dumped.

In some embodiments, the controller 1-107 controls the value of eachphase shifter in the photonic processor 1-103. Each phase shifterdiscussed above may include a DAC similar to the DACs discussed inconnection with the phase modulator 1-207 of the optical encoder 1-101.

The photonic processor 1-103 can include any number of input nodes, butthe size and complexity of the interconnected VBS arrays 1-301 and 1-305will increase as the number of input modes increases. For example, ifthere are n input optical modes, then the photonic processor 1-103 willhave a circuit depth of 2n+1, where the first matrix implementation1-301 and the second matrix implementation 1-305 each has a circuitdepth n and the second matrix implementation 1-303 has a circuit depthof one. Importantly, the complexity in time of performing a singlematrix multiplication is not even linear with the number of inputoptical pulses—it is always O(1). In some embodiments, this low ordercomplexity afforded by the parallelization results in energy and timeefficiencies that cannot be obtained using conventional electricalprocessors.

It is noted that, while embodiments described herein illustrate thephotonic processor 1-103 as having n inputs and n outputs, in someembodiments, the matrix M implemented by the photonic processor 1-103may not be a square matrix. In such embodiments, the photonic processor1-103 may have a different number of outputs and inputs.

It is also noted that, due to the topology of the interconnections ofthe VBSs within the first and second matrix implementations 1-301 and1-305, it is possible to subdivide the photonic processor 1-103 intonon-interacting subsets of rows such that more than one matrixmultiplication can be performed at the same time. For example, in theVBS array illustrated in FIG. 1-4, if each VBS 1-401 that couplesoptical modes 3 and 4 is set such that optical modes 3 and 4 do notcouple at all (e.g., as if the VBSs 1-401 with subscript “34” wereabsent from FIG. 1-4) then the top three optical modes would operatecompletely independently from the bottom three optical modes. Such asubdivision may be done at a much larger scale with a photonic processorwith a larger number of input optical modes. For example, an n=64photonic processor may multiply eight eight-component input vectors by arespective 8×8 matrix simultaneously (each of the 8×8 matrices beingseparately programmable and controllable). Moreover, the photonicprocessor 1-103 need not be subdivided evenly. For example, an n=64photonic processor may subdivide into seven different input vectors with20, 13, 11, 8, 6, 4, and 2 components, respectively, each multiplied bya respective matrix simultaneously. It should be understood that theabove numerical examples are for illustration purposes only and anynumber of subdivisions is possible.

Additionally, while the photonic processor 1-103 performs vector-matrixmultiplication, where a vector is multiplied by a matrix by passing theoptical signals through the array of VBSs, the photonic processor 1-103may also be used to perform matrix-matrix multiplication. For example,multiple input vectors may be passed through the photonic processor1-103, one after the other, one input vector at a time, where each inputvector represents a column of an input matrix. After optically computingeach of the individual vector-matrix multiplications (eachmultiplication resulting in an output vector that corresponds to acolumn of an output column of the resulting matrix), the results may becombined digitally to form the output matrix resulting from thematrix-matrix multiplication.

E. Optical Receiver

The photonic processor 1-103 outputs n optical pulses that aretransmitted to the optical receiver 1-105. The optical receiver 1-105receives the optical pulses and generates an electrical signal based onthe received optical signals. In some embodiments, the amplitude andphase of each optical pulse is determined. In some embodiments, this isachieved using homodyne or heterodyne detection schemes. In otherembodiments, simple phase-insensitive photodetection may be performedusing conventional photodiodes.

Referring to FIG. 1-9, the optical receiver 1-105 includes a homodynedetector 1-901, a transimpedance amplifier 1-903 and ananalog-to-digital converter (ADC) 1-905, according to some embodiments.While the components are shown as one element for all optical modes inFIG. 1-9, this is for the sake of simplicity. Each optical mode may havea dedicated homodyne detector 1-901, a dedicated transimpedanceamplifier 1-903 and a dedicated ADC 1-905. In some embodiments, atransimpedance amplifier 1-903 may not be used. Instead, any othersuitable electronic circuit that converts a current to a voltage may beused.

Referring to FIG. 1-10, the homodyne detector 1-903 includes a localoscillator (LO) 1-1001, a quadrature controller 1-1003, a beam splitter1-1005 and two detectors 1-1007 and 1-1009, according to someembodiments. The homodyne detector 1-903 outputs an electrical currentthat is based on the difference between the current output by the firstdetector 1-1007 and the second detector 1-1009.

The local oscillator 1-1001 is combined with the input optical pulse atthe beam splitter 1-1005. In some embodiments, a portion of the lightsource 1-201 is transmitted via an optical waveguide and/or an opticalfiber to the homodyne detector 1-901. The light from the light source1-201 may itself be used as the local oscillator 1-1001 or, in otherembodiments, the local oscillator 1-1001 may be a separate light sourcethat uses the light from the light source 1-201 to generate a phasematched optical pulse. In some embodiments, an MZI may replace the beamsplitter 1-1005 such that adjustments can be made between the signal andthe local oscillator.

The quadrature controller 1-1003 controls the cross-section angle inphase space in which the measurement is made. In some embodiments, thequadrature controller 1-1003 may be a phase shifter that controls therelative phase between the input optical pulse and the local oscillator.The quadrature controller 1-1003 is shown as a phase shifter in theinput optical mode. But in some embodiments, the quadrature controller1-1003 may be in the local oscillator mode.

The first detector 1-1007 detects light output by a first output of thebeam splitter 1-1005 and the second detector 1-1009 detects light outputby a second output of the beam splitter 1-1005. The detectors 1-1007 and1-1009 may be photodiodes operated with zero bias. A subtraction circuit1-1011 subtracts the electrical current from the first detector 1-1007from the electrical current from the second detector 1-1009. Theresulting current therefore has an amplitude and a sign (plus or minus).The transimpedance amplifier 1-903 converts this difference in currentinto a voltage, which may be positive or negative. Finally, an ADC 1-905converts the analog signal to a digital bit string. This output bitstring represents the output vector result of the matrix multiplicationand is an electrical, digital version of the optical outputrepresentation of the output vector that is output by the photonicprocessor 1-103. In some embodiments, the output bit string may be sentto the controller 1-107 for additional processing, which may includedetermining a next input bit string based on one or more output bitstrings and/or transmitting the output bit string to an externalprocessor, as described above.

The inventors have further recognized and appreciated that thecomponents of the above-described photonic processing system 1-100 neednot be chained together back-to-back such that there is a first matriximplementation 1-301 connected to a second matrix implementation 1-303connected to a third matrix implementation 1-305. In some embodiments,the photonic processing system 1-103 may include only a single unitarycircuit for performing one or more multiplications. The output of thesingle unitary circuit may be connected directly to the optical receiver1-105, where the results of the multiplication are determined bydetecting the output optical signals. In such embodiments, the singleunitary circuit may, for example, implement the first matriximplementation 1-301. The results detected by the optical receiver 1-105may then be transmitted digitally to a conventional processor (e.g.,processor 1-111) where the diagonal second matrix implementation 1-303is performed in the digital domain using a conventional processor (e.g.,1-111). The controller 1-107 may then reprogram the single unitarycircuit to perform the third matrix implementation 1-305, determine aninput bit string based on the result of the digital implementation ofthe second matrix implementation, and control the optical encoder totransmit optical signals, encoded based on the new input bit string,through the single unitary circuit with the reprogrammed settings. Theresulting output optical signals, which are detected by the opticalreceiver 105, are then used to determine the results of the matrixmultiplication.

The inventors have also recognized and appreciated that there can beadvantages to chaining multiple photonic processors 1-103 back-to-back,in series. For example, to implement a matrix multiplication M=M₁M₂,where M₁ and M₂ are arbitrary matrices but M₂ changes more frequentlythan M₁ based on a changing input workload, the first photonic processorcan be controlled to implement M₂ and the second photonic processorcoupled optically to the first photonic processor can implement M₁ whichis kept static. In this way, only the first photonic processing systemneeds to be frequently updated based on the changing input workload. Notonly does such an arrangement speed up the computation, but it alsoreduces the number of data bits that travel between the controller 1-107and the photonic processors.

F. Folded Photonic Processing System

In FIG. 1-1, in such an arrangement, the optical encoder 1-101 and theoptical receiver 1-105 are positioned on opposite sides of the photonicprocessing system 1-100. In applications where feedback from the opticalreceiver 1-105 is used to determine the input for the optical encoder1-101 for a future iteration of the process, the data is transferredelectronically from the optical receiver 1-105 to the controller 1-107and then to the optical encoder 1-101. The inventors have recognized andappreciated that reducing the distance that these electrical signalsneed to travel (e.g., by reducing the length of electrical traces and/orwires) results in power savings and lower latency. Moreover, there is noneed for the optical encoder 1-101 and optical receiver 1-105 to beplaced on opposite ends of the photonic processing system.

Accordingly, in some embodiments, the optical encoder 1-101 and theoptical receiver 1-105 are positioned near one another (e.g., on thesame side of the photonics processor 1-103) such that the distanceelectrical signals have to travel between the optical encoder 1-101 andthe optical receiver 1-105 is less than the width of the photonicsprocessor 1-103. This may be accomplished by physically interleavingcomponents of the first matrix implementation 1-301 and the third matriximplementation 1-305 such that they are physically in the same portionof the chip. This arrangement is referred to as a “folded” photonicprocessing system because the light first propagates in a firstdirection through the first matrix implementation 1-301 until it reachesa physical portion of the chip that is far from the optical encoder1-101 and the optical receiver 1-105, then folds over such that thewaveguides turn the light to be propagating in a direction opposite tothe first direction when implementing the third matrix implementation1-305. In some embodiments, the second matrix implementation 1-303 isphysically located adjacent to the fold in the waveguides. Such anarrangement reduces the complexity of the electrical traces connectingthe optical encoder 1-101, the optical receiver 1-105, and thecontroller 1-107 and reduces the total chip area used to implement thephotonic processing system 1-100. For example, some embodiments usingthe folded arrangement only use 65% of the total chip area that would beneeded if the back-to-back photonic arrangement of FIG. 1-1 was used.This may reduce the cost and complexity of the photonic processingsystem.

The inventors have recognized and appreciated that there are not onlyelectrical advantages to a folded arrangement, but also opticaladvantages. For example, by reducing the distance that the light signalhas to travel from the light source to be used as a local oscillator forthe homodyne detection, the time-dependent phase fluctuations of theoptical signal may be reduced, resulting in higher quality detectionresults. In particular, by locating the light source and the homodyne onthe same side of the photonics processor, the distance traveled by thelight signal used for the local oscillator is no longer dependent on thesize of the matrix. For example, in the back-to-back arrangement of FIG.1-1, the distance traveled by the light signal for the local oscillatorscales linearly with the size of the matrix, whereas the distancetraveled in the folded arrangement is constant, irrespective of thematrix size.

FIG. 1-11 is a schematic drawing of a folded photonic processing system1-1100, according to some embodiments. The folded photonic processingsystem 1-1100 includes a power tree 1-1101, a plurality of opticalencoders 1-1103 a-1-1103 d, a plurality of homodyne detectors 1-1105a-1-1105 d, a plurality of selector switches 1-1107 a-1-1107 d, aplurality of U-matrix components 1-1109 a-1-1109 j, a plurality ofdiagonal-matrix components 1-1111 a-1-1111 d, and a plurality ofV-matrix components 1-1113 a-1-1113 j. For the sake of clarity, not allcomponents of the folded photonic processing system are shown in thefigure. It should be understood that the folded photonic processingsystem 1-1100 may include similar components as the back-to-backphotonic processing system 1-100.

The power tree 1-1101 is similar to the power tree 1-203 of FIG. 2 andis configured to deliver light from a light source (not shown) to theoptical encoders 1-1103. However, a difference in the power tree 1-1101and the power tree 1-203 is that the power tree delivers optical signalsto the homodyne detectors 1-1105 a directly. In FIG. 2, the light source201 delivers a local oscillator signal to the homodyne detectors on theother side of the photonic processor by tapping off a portion of theoptical signal from the light source and guiding the optical signalusing a waveguide. In FIG. 1-11, the power tree 1-1101 includes a numberof outputs that is equal to twice the number of spatial modes. Forexample, FIG. 1-11 illustrates only four spatial modes of a photonicprocessor, which results in eight output modes from the power tree1-1101—one output directing light to each optical encoder 1-1103 and oneoutput directing light to each homodyne detector 1-1105. The power treemay be implemented, for example, using cascading beam splitters or amultimode interferometer (MMI).

The optical encoders 1-1103 are similar to the power tree opticalencoder 1-101 of FIG. 1 and are configured to encode information intothe amplitude and/or phase of the optical signals received from thepower tree 1-1101. This may be achieved, for example as described inconnection with the optical encoder 1-101 of FIG. 2.

The homodyne detectors 1-1105 are located between the power tree 1-1101and the U-matrix components 1-1109. In some embodiments, the homodynedetectors 1-1105 are physically positioned in a column with the opticalencoder 1-1103. In some embodiments, the optical encoders 1-1103 and thehomodyne detectors 1-1105 may be interleaved in a single column. In thisway, the optical encoders 1-1103 and the homodyne detectors 1-1105 arein close proximity to one another, reducing the distance of electricaltraces (not shown) used to connect the optical encoders 1-1103 and thehomodyne detectors 1-1105 and a controller (not shown) which may bephysically located adjacent to the column of the optical encoders 1-1103and the homodyne detectors 1-1105.

Each of the optical encoders 1-1103 is associated with a respectivehomodyne detector 1-1105. Both the optical encoders 1-1103 and thehomodyne detectors 1-1105 receive optical signals from the power tree1-1101. The optical encoders 1-1103 use the optical signals to encode aninput vector, as described above. The homodyne detectors 1-1105 use thereceived optical signals received from the power tree as the localoscillator, as described above.

Each pair of the optical encoders 1-1103 and the homodyne detectors1-1105 is associated with and connected to a selector switch 1-1107 by awaveguide. The selector switches 1-1107 a-1-1107 d may be implementedusing, for example, a conventional 2×2 optical switch. In someembodiments, the 2×2 optical switch is a MZI with an internal phaseshifter to control the MZI's behavior from a crossing to a bar. Theswitch 1-1107 is connected to a controller (not shown) to controlwhether an optical signal received from the optical encoder 1-1103 willbe guided towards the U-matrix components 1-1109 or the V-matrixcomponents 1-1113. The optical switch is also controlled to guide lightreceived from the U-matrix components 1-1109 and/or the V-matrixcomponents 1-1113 toward the homodyne detectors 1-1105 for detection.

The techniques for implementing matrix multiplication is similar in thephotonic folded photonic processing system 1-1100 as was described abovein connection with the back-to-back system, described in FIG. 1-3. Adifference between the two systems is in the physical placement of thematrix components and the implementation of a fold 1-1120, where theoptical signals change from propagating approximately left to right inFIG. 1-11 to propagating approximately right to left. In FIG. 1-11, theconnections between components may represent waveguides. The solid-linedconnections represent portions of waveguide where the optical signalsare propagating from left to right, in some embodiments, and thedashed-lined connections represent portions of waveguide where theoptical signals are propagating from right to left, in some embodiments.In particular, given this nomenclature, the embodiment illustrated inFIG. 1-11 is an embodiment where the selector switches 1-1107 guide theoptical signals to the U-matrix components 1-1109 first. In otherembodiments, the selector switches 1-1107 may guide the optical signalsto the V-matrix components 1-1113 first, in which case the dashed lineswould represent portions of waveguide where the optical signals arepropagating from left to right, and the solid-lined connections wouldrepresent portions of waveguide where the optical signals arepropagating from right to left.

The U-matrix of the SVD of a matrix M is implemented in photonicprocessing system 1-1100 using U-matrix components 1-1109 that areinterleaved with the V-matrix components 1-1113. Thus, unlike theembodiment of the back-to-back arrangement illustrated in FIG. 1-3, allof the U-matrix components 1-1109 and the V-matrix components 1-1113 arenot physically located in a respective self-contained array within asingle physical area. Accordingly, in some embodiments, the photonicprocessing system 1-1100 includes a plurality of columns of matrixcomponents and at least one of the columns contains both U-matrixcomponents 1-1109 and V-matrix components 1-1113. In some embodiments,the first column may only have U-matrix components 1-1109, asillustrated in FIG. 1-11. U-matrix components 1-1109 are implementedsimilarly to the first matrix implementation 1-301 of FIG. 3.

Due to the interleaving structure of the U-matrix components 1-1109 andthe V-matrix components 1-1113, the folded photonic processing system1-1100 includes waveguide crossovers 1-1110 at various locations betweenthe columns of matrix elements. In some embodiments, the waveguidecrossovers can be constructed using adiabatic evanescent elevatorsbetween two or more layers in an integrated photonics chip. In otherembodiments, the U-matrix and the V-matrix may be positioned ondifferent layers of the same chip and the waveguide crossovers are notused.

After optical signals propagate through all of the U-matrix components1-1109, the optical signals propagate to the diagonal-matrix components1-1111, which are implemented similarly to the second matriximplementation 1-303 of FIG. 1-3.

After optical signals propagate through all of the diagonal-matrixcomponents 1-1111, the optical signals propagate to the V-matrixcomponents 1-1113, which are implemented similarly to the third matriximplementation 1-305 of FIG. 1-3. The V-matrix of the SVD of a matrix Mis implemented in photonic processing system 1-1100 using V-matrixcomponents 1-1113 that are interleaved with the U-matrix components1-1109. Thus, all of the V-matrix components 1-1113 are not physicallylocated in a single self-contained array.

After the optical signals propagate through all of the V-matrixcomponents 1-1113, the optical signals return to the selector switch1-1107, which guides the optical signals to the homodyne detectors1-1105 for detection.

The inventors have further recognized and appreciated that by includingselector switches after the optical encoders and before the matrixcomponents, the folded photonic processing system 1-1100 allowsefficient bi-directionality of the circuit. Thus, in some embodiments, acontroller, such as the controller 1-107 described in connection withFIG. 1-1, may control whether the optical signals are multiplied by theU matrix first or the V^(T) matrix first. For an array of VBSs set toimplement a unitary matrix U when propagating the optical signals fromleft to right, propagating the optical signals from right to leftimplements a multiplication by a unitary matrix U^(T). Thus, the samesettings for an array of VBSs can implement both U and U^(T) dependingwhich way the optical signals are propagated through the array, whichmay be controlled using the selector switch in 1-1107. In someapplications, such as back-propagation used to train a machine learningalgorithm, it may be desirable to run optical signals through one ormore matrices backwards. In other applications, the bi-directionalitycan be used to compute the operation of an inverted matrix on an inputvector. For example, for an invertible n×n matrix M, an SVD results inM=V^(T)ΣU. The inverse of this matrix is M⁻¹=U^(T)Σ⁻¹V, where Σ⁻¹ is theinverse of a diagonal matrix which can be computed efficiently byinverting each diagonal element. To multiply a vector by the matrix M,the switches are configured to direct the optical signals through thematrix U, then Σ, then V^(T) in a first direction. To multiply a vectorby the inverse M⁻¹, the singular values are first set to program theimplementation of the τ⁻¹ matrix. This constitutes changing the settingsof only one column of VBSs instead of all 2n+1 columns of the photonicprocessor, which is the case for a single-directional photonicprocessing system such as the one illustrated in FIG. 1-3. The opticalsignals representing the input vector are then propagated through thematrix V^(T), then Σ⁻¹, and then U in a second direction that isopposite the first direction. Using the selector switches 1-1107, thefolded photonic processing system 1-1100 may be easily changed fromimplementing the U matrix (or its transpose) first and implementing theV^(T) matrix (or its transpose) first.

G. Wavelength Division Multiplexing

The inventors have further recognized and appreciated that there areapplications where different vectors may be multiplied by the samematrix. For example, when training or using machine learning algorithmssets of data may be processed with the same matrix multiplications. Theinventors have recognized and appreciated that this may be accomplishedwith a single photonic processor if the components before and after thephotonic processor are wavelength-division-multiplexed (WDM).Accordingly, some embodiments include multiple frontends and backends,each associated with a different wavelength, while only using a singlephotonic processor to implement the matrix multiplication.

FIG. 1-12A illustrates a WDM photonic processing system 1-1200,according to some embodiments. The WDM photonic processing system 1-1200includes N frontends 1-1203, a single photonic processor 1-1201 with Nspatial modes, and N backends 1-1205.

The photonic processor 1-1201 may be similar to the photonic processor1-103, with N input modes and N output modes. Each of the N frontends1-1203 is connected to a respective input mode of photonic processor1-1201. Similarly, each of the N backends 1-1205 is connected to arespective output mode of photonic processor 1-1201.

FIG. 1-12B illustrates details of at least one of the frontends 1-1203.As with the photonic processing system of other embodiments, thephotonic processing system 1-1200 includes optical encoders 1-1211. Butin this embodiment, there are M different optical encoders, where M isthe number of wavelengths being multiplexed by the WDM photonicprocessing system 1-1200. Each of the M optical encoders 1-1211 receiveslight from a light source (not shown) that generates the M opticalsignals, each of a different wavelength. The light source may be, forexample, an array of lasers, a frequency comb generator or any otherlight source that generates coherent light at different wavelengths.Each of the M optical encoders 1-1211 is controlled by a controller (notshown) to implement an appropriate amplitude and phase modulation toencode data into the optical signal. Then, the M encoded optical signalsare combined into a single waveguide using an M:1 WDM 1-1213. The singlewaveguide then connects to the one of the N waveguides of the photonicprocessor 1-1201.

FIG. 1-12C illustrates details of at least one of the backends 1-1205.As with the photonic processing system of other embodiments, thephotonic processing system 1-1200 includes detectors 1-1223, which maybe phase-sensitive or phase-insensitive detectors. But in thisembodiment, there are M different detectors 1-1223, where M is thenumber of wavelengths being multiplexed by the WDM photonic processingsystem 1-1200. Each of the M detectors 1-1223 receives light from a 1:MWDM 1-1221, which splits the single output waveguide from photonicprocessor 1-1201 into M different waveguides, each carrying an opticalsignal of a respective wavelength. Each of the M detectors 1-1223 may becontrolled by a controller (not shown) to record measurement results.For example, each of the M detectors 1223 may be a homodyne detector ora phase insensitive photodetector.

In some embodiments, the VBSs in the photonic processor 1-1201 may bechosen to be non-dispersive within the M wavelengths of interest. Assuch, all the input vectors are multiplied by the same matrix. Forexample, an MMI can be used instead of a directional coupler. In otherembodiments, the VBSs may be chosen to be dispersive within the Mwavelengths of interest. In some applications related to stochasticoptimization of the parameters of a neural network model, this isequivalent to adding noise when computing the gradient of theparameters; increased gradient noise may be beneficial for fasteroptimization convergence and may improve the robustness of a neuralnetwork.

While FIG. 1-12A illustrates a back-to-back photonic processing system,similar WDM techniques may be used to form a WDM folded photonicprocessor using the techniques described in relation to folded photonicprocessor 1-1100.

H. Analog Summation of Outputs

The inventors have recognized and appreciated that there areapplications where it is useful to calculate the sum or the average ofthe outputs from the photonic processor 1-103 over time. For example,when the photonic processing system 1-100 is used to compute a moreexact matrix-vector multiplication for a single data point, one may wantto run a single data point through the photonic processor multiple timesto improve the statistical results of the calculation. Additionally oralternatively, when computing the gradient in a backpropagation machinelearning algorithm, one may not want a single data point determining thegradient, so multiple training data points may be run through photonicprocessing system 1-100 and the average result may be used to calculatethe gradient. When using a photonic processing system to perform abatched gradient based optimization algorithm, this averaging canincrease the quality of the gradient estimate and thereby reduce thenumber of optimization steps required to achieve a high qualitysolution.

The inventors have further recognized and appreciated that the outputsignals may be summed in the analog domain, before converting theoutputs to digital electrical signals. Thus, in some embodiments, a lowpass filter is used to sum the outputs from the homodyne detectors. Byperforming the summation in the analog domain, the homodyne electronicsmay use a slow ADC rather than a costlier fast ADC (e.g., an ADC withhigh power consumption requirements) that would be required to perform asummation in the digital domain.

FIG. 1-13 illustrates a portion of an optical receiver 1-1300 and how alow pass filter 1-1305 may be used with a homodyne detector 1-1301,according to some embodiments. The homodyne detector 1-1301 performs ameasurement of the field and phase of an incoming optical pulse. If k isthe label for the different input pulses over time and there is a totalof K inputs, the sum over k can be automatically performed in the analogdomain using low-pass filter 1-1305. The main difference between thisoptical receiver 1-1300 and the optical receiver 1-105 illustrated inFIG. 1-9 is that the low-pass filter is after the transimpedanceamplifier 1-1303 after the output of the homodyne detector. If a totalof K signals (with components y_(i) ^((k))) arrives at the homodynedetector within a single slow sampling period T_(s) ^((slow)), thelow-pass filter will have accumulated/removed the charges in thecapacitor C according to the sign and value of y_(i) ^((k)). The finaloutput of the low-pass filter is proportional to Y_(i)=Σ_(k=1) ^(K)y_(i)^((k)), which can be read once with a slower ADC (not shown) with asampling frequency of f_(s) ^((slow))=1/T_(s) ^((slow))=f_(s)/K, wheref_(s) is the originally required sampling frequency. For an idealsystem, the low-pass filter should have a 3-dB bandwidth: f_(3 dB)=f_(s)^((slow))/2. For a low-pass filter using an RC circuit as shown in theembodiment of FIG. 1-13, f_(3 dB)=1/(2πRC), and the values of R and Ccan be chosen to obtain the desired sampling frequency: f_(s) ^((slow)).

In some embodiments both a fast ADC and a slow ADC may be present. Inthis context, a fast ADC is an ADC that is configured to receive andconvert each individual analog signal into a digital signal (e.g., anADC with a sampling frequency equal to or greater than the frequency atwhich the analog signals arrive at the ADC), and a slow ADC is an ADCthat is configured to receive multiple analog signals and convert thesum or average of multiple received analog signals into a single digitalsignal (e.g., an ADC with a sampling frequency less than the frequencyat which the analog signals arrive at the ADC). An electrical switch maybe used to switch the electrical signal from the homodyne detector andpossibly transimpedance amplifier to the low-pass filter with a slow ADCor to the fast ADC. In this way, the photonic processing system of someembodiments may switch between performing analog summation using theslow ADC and measuring every optical signal using the fast ADC.

I. Stabilizing Phases

The inventors have recognized and appreciated that it is desirable tostabilize the phase of the local oscillator used for performingphase-sensitive measurements (e.g., homodyne detection) to ensureaccurate results. The photonic processors of the embodiments describedherein perform matrix operations by interfering light between N distinctspatial modes. The results are measured, in some embodiments, with phasesensitive detectors, such as homodyne or heterodyne detectors. Thus, toensure the matrix operations are accurately performed, the phaseimparted at various portions of the photonic processor should be asaccurate as possible and the phase of the local oscillator used toperform phase-sensitive detection should be precisely known.

The inventors have recognized and appreciated that parallel interferenceoperations, such as those performed within a single column of VBSs ofthe photonic processor, must not only impart the correct phases usingthe phase modulators controlling the relative phase within the MZI ofthe VBS and the phase and the relative phase of the output of the MZI,but each VBS in a column should impart the same global phase shiftacross all the spatial modes of photonic processor. In this application,the global phase shift for a column of VBSs in the photonic processor isreferred to as the “column-global phase.” The column-global phase is thephase imparted due to effects not related to the programmed phasesassociated with the VBS, such as phases imparted due to propagationthrough the waveguide or phases due to temperature shifts. These phasesneed not be imparted exactly simultaneously within a column on VBSs, butonly need be imparted as a result of traversing the column in question.Ensuring the column-global phase is uniform between the differentspatial modes of the column is important because the output opticalsignals from one column will likely be interfered at one or more VBSs ata subsequent column. The subsequent interference—and therefore theaccuracy of the calculation itself—would be incorrect if thecolumn-global phase at the previous columns is not uniform.

FIG. 1-14 illustrates the column-global phases and total global phasefor a photonic processing system 1-1400. Similar to the above-describedembodiments of photonic processing systems, the photonic processingsystem 1-1400 includes a U-matrix implementation 1-1401, a diagonalmatrix implementation 1-1403, a V-matrix implementation 1-1405, and aplurality of detectors 1-1407 a-1-1407 d. These implementations aresimilar to the first, second and third matrix implementations describedabove. For the sake of simplicity, only four modes of the photonicprocessing system 1-1400 are shown, though it should be understood thatany larger number of modes may be used. Also for simplicity, only theVBSs associated with the U-matrix implementation 1-1401 are illustrated.The arrangement of components of the diagonal matrix implementation1-1403 and a V-matrix implementation 1-1405 are similar to the third andfourth matrix implementations described above.

The U-matrix implementation 1-1401 includes a plurality of VBSs 1-1402,though only a single VBS 1-1402 is labeled for the sake of clarity. TheVBSs are labeled, however, with subscripts that identify which opticalmodes are being mixed by a particular VBS and a superscript labeling theassociated column.

As illustrated in FIG. 1-14, each column is associated with acolumn-global phase that is ideally uniform for every element of thecolumn. For example, column 1 of the U-matrix implementation 1-1401 isassociated with a column-global phase ϕ_(U) ₁ , column 2 of the U-matriximplementation 1-1401 is associated with a column-global phase ϕ_(U) ₂ ,column 3 of the U-matrix implementation 1-1401 is associated with acolumn-global phase ϕ_(U) ₃ , and column 4 of the U-matriximplementation 1-1401 is associated with a column-global phase ϕ₄.

In some embodiments, the column-global phases can be made uniform atleast in part by implementing each VBS 1-1402 as a MZI in a push-pullconfiguration. Alternatively or additionally, external phase shifter canbe added to the output of each MZI to correct for any phase errorimparted from the internal phase elements of the MZIs (e.g., the phaseshifters).

The inventors have further recognized and appreciated that even if theconditions are such that each column of the photonic processing system1-1400 provides a uniform column-global phase, phases can be accrued asthe signal propagates from the first column to the last. There is aglobal U-matrix phase, ϕ_(U), associated with the entire U-matriximplementation 1-1401 and is equal to the sum of the individualcolumn-global phase. Similarly, the diagonal-matrix implementation1-1403 is associated with a global diagonal-matrix phase, ϕ_(Σ), and theV-matrix implementation 1-1405 is associated with a globaldiagonal-matrix phase, ϕ_(V) _(†) . A total global phase ϕ_(G) for theentire photonic processing system 1-1400 is then given by the sum of thethree individual global matrix phases. This total global phase may beset to be uniform between all the output modes, but the local oscillatorthat is used for phase-sensitive detection did not propagate through thephotonic processor and did not experience this total global phase. Thetotal global phase ϕ_(G), if not accounted for, can lead to an error inthe values read out by the homodyne detectors 1-1407 a-1-1407 d.

The inventors have further recognized that errors in the multiplicationoperation may result from changes in temperature, which change awaveguide's effective refractive index n_(eff). Accordingly, in someembodiments, either the temperature of each column is set to be uniformor stabilization circuits can be placed at each column such that thephases imparted to all the modes of a single column are actively tunedto be uniform. Additionally, as the light signal for the localoscillator propagates through a different part of the system, thetemperature difference between different parts of the system can causeerrors in the phase-sensitive measurements. The amount of phasedifference between the signal and the local oscillator is

${\phi_{T} = {\frac{2\pi}{\lambda}\left( {{{n_{eff}\left( T_{s} \right)}L_{s}} - {{n_{eff}\left( T_{LO} \right)}L_{LO}}} \right)}},$

where T_(s) and T_(LO) are the temperatures of the signal waveguide inthe photonic processor and the local oscillator waveguide, respectively,n_(eff)(T) is the effective index of refraction as a function oftemperature, λ is the average wavelength of the light, and L_(s) andL_(LO) are the propagation lengths through the signal waveguide in thephotonic processor and the local oscillator waveguide, respectively.Assuming that the difference in temperature ΔT=T_(LO)−T_(S) is small,then the effective index can be rewritten as:

${{n_{eff}\left( T_{LO} \right)} \approx {{n_{eff}\left( T_{S} \right)} + \frac{{dn}_{eff}}{dT}}}❘_{T = T_{S}}{\Delta\;{T.}}$

Therefore, the phase difference between the signal and the LO can bewell approximated by

${\Phi_{T} = {{\frac{2\pi}{\lambda}\frac{{dn}_{eff}}{dT}}❘_{T = T_{S}}{{L \cdot \Delta}\; T}}},$

which increases linearly with longer propagation length L. Therefore,for a sufficiently long propagation distance, a small change intemperature can result in a large phase shift (on the order of oneradian). Importantly, the values of L_(S) does not need to be the sameas the value of L_(LO), and the maximum difference between the two isdetermined by the coherence length of the light source L_(coh). For alight source with a bandwidth of Δv, the coherence length can be wellapproximated by L_(coh)≈c_(eff)Δv, where c_(eff) is the speed of lightin the transmission medium. As long as the length difference betweenL_(S) and L_(LO) is much shorter than L_(coh), interference between thesignal and the local oscillator will be possible for the correctoperation of the photonic processing system.

Based on the foregoing, the inventors have identified at least twosources of possible phase errors between the output signals of thephotonic processor and the local oscillator used for homodyne detectionin some embodiments. Thus, where an ideal homodyne detector wouldmeasure the magnitude and phase of the signal output by subtracting theoutputs of the two photodetectors, resulting in a phase sensitiveintensity output measurement of I_(out)∝|E_(S)∥E_(LO)| cos(θ_(s)−θ_(LO)+ϕ_(G)+ϕ_(T)), where E_(S) is the electric field magnitudeof the optical signal from the output of the photonic processor, E_(LO)is the electric field magnitude of the local oscillator, θ_(S) is thephase shift imparted by the photonic processor that is desired to bemeasured, ϕ_(G) is the total global phase, and ϕ_(T) is the phase shiftcaused by temperature differences between the local oscillator and theoptical signal. Consequently, if the total global phase and the phaseshift due to temperature differences are not accounted for, the resultof the homodyne detection can be erroneous. Therefore, in someembodiments the total systematic phase error, ΔΦ=Φ_(G)+Φ_(T), ismeasured and the system is calibrated based on that measurement. In someembodiments, the total systematic phase error includes contributionsfrom other sources of error that are not necessarily known oridentified.

According to some embodiments, the homodyne detectors may be calibratedby sending pre-computed test signals to the detectors and using thedifference between the pre-computed test signals and the measured testsignals to correct for the total systematic phase error in the system.

In some embodiments, rather than considering the total global phase,Φ_(G), and the phase shift caused by temperature differences, Φ_(T), asbeing related to the optical signals propagating through the photonicprocessor, they can be described as the signal not accruing any phaseshift at all but the LO having a total systematic phase error −ΔΦ. FIG.1-15 illustrates the effect on the results of the homodyne measurementsin such a situation. The original (correct) vector of quadrature valuesof the signal [x, p]^(T) is rotated by a rotation matrix parameterizedby ΔΦ producing an incorrect quadrature values of [x′, p′^(T)].

Based on the rotation in quadrature due to the total systematic error,in some embodiments, the value of ΔΦ is obtained as follows. First, avector {right arrow over (v_(in))} is selected (e.g., a random vector),using, e.g., the controller 1-107. The vector is of a type that can beprepared by the optical encoders of the photonic processing system.Second, the output value of {right arrow over (v_(out))}=M {right arrowover (v_(in))}, where M is the matrix implemented by the photonicprocessor in the ideal case assuming that there is no unaccounted phaseaccrued of ΔΦ, is calculated using, for example, the controller 1-107 orsome other computing device. As a result, each element of {right arrowover (v_(out))} corresponds to x_(k)+ip_(k), where k labels each of theoutput modes of the photonic processor.

In some embodiments, loss in propagating the random vector through thephotonic processor may be considered when calculating the theoreticalprediction x_(k)+ip_(k). For example, for a photonic processor withtransmission efficiency η, the field signal of x_(k)+ip_(k) will become√{square root over (η)}(x_(k)+ip_(k)).

Next, the random vector {right arrow over (v_(in))} is prepared by theoptical encoder of the actual system, propagated through the photonicprocessor, and each element of the output vector is measured in bothquadratures to obtain x_(k)′+ip_(k)′. The phase difference ΔΦ_(k)between the local oscillator and the signal of output mode k is given by

${\Delta\Phi}_{k} = {{\tan^{- 1}\left( \frac{{p_{k}\prime\; x_{k}} - {x_{k}\prime\; p_{k}}}{{x_{k}x_{k}\prime} + {p_{k}p_{k}\prime}} \right)}.}$

Generally, the phase difference ΔΦ_(k)≠ΔΦ_(l) for k≠l as the path lengthof the LO to the detector for mode k can be different to that for model).

Finally, the local oscillator phase shifter used to select themeasurement quadrature of the homodyne detector is controlled to impartθ_(LO,k)=ΔΦ_(k). As a result, the axes (x, p) will align with the axes(x′, p′), as illustrated in FIG. 1-15. The calibration may be checked atthis stage to ensure it is accurate by propagating the vector {rightarrow over (v_(in))} once again to see that the obtained measurementresults are equal to the predicted {right arrow over (v_(out))} whenboth quadratures are measured.

Generally, the value of ΔΦ_(k) can be determined more precisely if thefield amplitude |E_(S,k)|=√{square root over (x_(k) ²+p_(k) ²)}=√{squareroot over (x_(k)′²+p_(k)′²)} is as large as possible. For example, ifthe field E_(S,k) is considered to be a coherent signal, e.g. from alaser source, then the optical signal may be theoretically modeled as acoherent state. The intuitive picture is given in FIG. 1-16, where thesignal is the amplitude |E_(S,k)| and the noise is given by the standarddeviation of the Gaussian coherent state. The coherent state |α_(k)> inmode k is the eigenstate of the annihilation operator a_(k), i.e.a_(k)|α_(k)>=α_(k)|α_(k)>. The electric field of mode k with a singlefrequency ω is described by E_(S,k) ⁽⁺⁾(t)=a_(k)e^(−iωt), which is alsoan eigenstate of the coherent state: E_(S,k)⁽⁺⁾(t)|α_(k))=α_(k)e^(−iωt)|α_(k)>. A homodyne detector with a localoscillator of the same frequency ω performs the quadrature measurementsx_(k)=(a_(k)+a_(k) ^(†))/2 when θ_(LO)=0 and p_(k)=(a_(k)−a_(k) ^(†))/2iwhen θ_(LO)=π/2. An ideal homodyne detector will find that thesemeasurements have an intrinsic quantum noise of √{square root over(<Δx_(k) ²>)}=½ and √{square root over (<Δp_(k) ²>)}=½. This noise isrelated to the quantum uncertainties, and it can be reduced by squeezingon the quadratures. The precision at which the angle ΔΦ_(k) can bedetermined is directly related to the signal-to-noise ratio (SNR) ofthese measurements. For a coherent-state signal E_(S,k) with a total ofN_(ph) photons (i.e. E_(S,k)=|√{square root over (N_(ph))}^(e) ^(iθ)^(s)>, the SNR of both x_(k) and p_(k) is upper bounded by:

${SNR}_{x} = {{\frac{< x_{k} >^{2}}{< {\Delta\; x_{k}} >^{2}} \leq {4N_{ph}\mspace{14mu}{and}\mspace{14mu}{SNR}_{p}}} = {\frac{< p_{k} >^{2}}{< {\Delta\; p_{k}} >^{2}} \leq {4{N_{ph}.}}}}$

(The bound of SNR_(x) is saturated when θ_(S)=0 or π, and the bound onSNR_(p) is saturated when θ_(S)=π/2 or 3π/2). Therefore, to increase theSNR and to determine the values of ΔΦ_(k) more accurately, someembodiments may propagate a few different choices of vector {right arrowover (v_(in))} (e.g., multiple different random vectors). In someembodiments, the choices of {right arrow over (v_(in))} are chosen tomaximize the amplitude |E_(S,k)|=N_(ph) for one value of k at a time.

There may be phase drift during the operation of the photonic processingsystem, e.g., due to temperature fluctuations over time. Thus, in someembodiments, the aforementioned calibration procedure may be performedrepeatedly during the operation of the system. For example, in someembodiments, the calibration procedure is performed regularly at a timescale that is shorter than the natural timescale of the phase drift.

The inventors have further recognized and appreciated that it ispossible to perform signed matrix operations without the need ofphase-sensitive measurements at all. Therefore, in applications, eachhomodyne detector at each output mode may be replaced by a directphotodetector which measures the intensity of the light at that outputmode. As there is no local oscillator in such a system, the systematicphase error ΔΦ is non-existent and meaningless. Thus, according to someembodiments, phase-sensitive measurements, such as homodyne detection,may be avoided such that the systematic phase error is insignificant.For example, when computing matrix operations of signed matrices andvectors, complex matrices and vectors, and hypercomplex (quaternion,octonion, and other isomorphisms (e.g., elements of unital algebra))matrices and vectors using unsigned matrices do not requirephase-sensitive measurements.

To illustrate how phase-sensitive measurements are not necessary,consider the case of performing matrix multiplication between a signedmatrix M and a signed vector {right arrow over (v_(in))}. To compute thevalue of signed output {right arrow over (v_(out))}=M {right arrow over(v_(in))}, the following procedure may be performed by, for example, thecontroller 1-107. First, the matrix M is split into M₊ and M⁻, whereM₊(M⁻) is a matrix that contains all the positive (negative) entries ofM. In this case, M=M₊−M⁻. Second, the vector is split in a similarmanner such that the vector {right arrow over (v_(in))}={right arrowover (v_(in+))}−{right arrow over (v_(in−))}, where {right arrow over(v_(in,+))} ({right arrow over (v_(in,−))}) is a vector that containsall the positive (negative) entries of {right arrow over (v_(in,))}. Asa result of the splittings, {right arrow over (v_(out))}=M{right arrowover (v_(in))}=(M₊−M⁻)({right arrow over (v_(in,+))}−{right arrow over(v_(in,−))})=(M₊{right arrow over (v_(in,+))}+M⁻{right arrow over(v_(in,−))})−(M₊{right arrow over (v_(in,−))}+M⁻{right arrow over(v_(in,−))}). Each term of this final equation corresponds to a separateoperation (M₊{right arrow over (v_(in,+))}, M⁻{right arrow over(v_(in,−))}, M₊{right arrow over (v_(in,−))}, and M⁻{right arrow over(v_(in,−))}) that may be performed individually by the photonicprocessing system. The output of each operation is a vector of a single(positive) sign, and therefore can be measured using a direct detectionscheme without the need for homodyne detection. The photodetector schemewill measure the intensity, but the square root of the intensity may bedetermined, resulting in the electric field amplitude. In someembodiments, each operation is performed separately and the results arestored in a memory (e.g., memory 1-109 of controller 1-107) until all ofthe separate operations are performed and the results may be digitallycombined to obtain the final result of the multiplication, {right arrowover (v_(out))}.

The above scheme works because M₊ and M⁻ are both matrices of allpositive entries. Similarly, {right arrow over (v_(in,+))} and {rightarrow over (v_(in,−))} are both vectors of all positive entries.Therefore, the results of their multiplications will be vectors of allpositive entries—regardless of the combination.

The inventors have further recognized and appreciated that the abovesplitting technique may be extended to complex-valued vectors/matrices,quaternion-valued vectors/matrices, octonion-valued vectors/matrices,and other hypercomplex representations. Complex numbers employ twodifferent fundamental units {1, i}, Quaternions employ four differentfundamental units {1, i, j, k}, and octonions employ eight fundamentalunits {e₀≡1, e₁, e₂, . . . , e₇}.

In some embodiments, a complex vector may be multiplied by a complexmatrix without the need for phase-sensitive detection by splitting themultiplication into separate operations similar to the proceduredescribed above for signed matrices and vectors. In the case of complexnumbers, the multiplication splits into 16 separate multiplications ofall-positive matrices and all-positive vectors. The results of the 16separate multiplications may then be digitally combined to determine theoutput vector result.

In some embodiments, a quaternion-valued vector may be multiplied by aquaternion-valued matrix without the need for phase-sensitive detectionby splitting the multiplication into separate operations similar to theprocedure described above for signed matrices and vectors. In the caseof quaternion-valued numbers, the multiplication splits into 64 separatemultiplications of all-positive matrices and all-positive vectors. Theresults of the 64 separate multiplications may then be digitallycombined to determine the output vector result.

In some embodiments, a octonion-valued vector may be multiplied by aoctonion-valued matrix without the need for phase-sensitive detection bysplitting the multiplication into separate operations similar to theprocedure described above for signed matrices and vectors. In the caseof octonion-valued numbers, the multiplication splits into 256 separatemultiplications of all-positive matrices and all-positive vectors. Theresults of the 256 separate multiplications may then be digitallycombined to determine the output vector result.

The inventors have further recognized and appreciated thattemperature-dependent phase Φ_(T) can be corrected by placing atemperature sensor next to each MZI of the photonic processor. Theresults of the temperature measurement may then be used as an input to afeedback circuitry that controls the external phases of each MZI. Theexternal phases of the MZI are set to cancel the temperature-dependentphase accrued at every MZI A similar temperature feedback loop can beused on the local oscillator propagation path. In this case, thetemperature measurement results are used to inform the settings of thehomodyne detector quadrature-selecting phase shifter to cancel the phaseaccrued by the local oscillator due to detected temperature effects.

In some embodiments, the temperature sensors can be those conventionallyused in semiconductor devices, e.g. p-n junction or bipolar junctiontransistor, or they can be photonic temperature sensors, e.g. usingresonators whose resonance changes with temperatures. Externaltemperature sensors such as thermocouples or thermistors may also beused in some embodiments.

In some embodiments, the phases accrued may be directly measured by, forexample, tapping some light at every column and performing homodynedetection with the same global local oscillator. This phase measurementcan directly inform the values of external phases used at each MZI tocorrect for any phase error. In the case of directly measured phaseerrors, the errors do not need to be column-global to be corrected.

J. Intermediary Computation for Large Data

The inventors have recognized and appreciated that the matrix vectorproduct performed by the photonic processor 1-103, and/or any otherphotonic processor according to other embodiments described in thepresent disclosure, can be generalized into tensor (multidimensionalarray) operations. For example, the core operation of M{right arrow over(x)} where M is a matrix and {right arrow over (x)} is a vector can begeneralized into a matrix-matrix product: MX where both M and X arematrices. In this particular example, consider the n-by-m matrix X to bea collection of m column vectors each consisting of n elements, i.e.X=[{right arrow over (x₁)}, {right arrow over (x₂)}, . . . , {rightarrow over (x_(m))}]. A photonic processor can complete thematrix-matrix product MX one column vector at a time with a total of mmatrix-vector products. The computation can be distributed amongmultiple photonic processors as the computation is a linear operation,which is perfectly parallelizable, e.g., any one matrix-vector productoutput does not depend on the results of the other matrix-vectorproducts. Alternatively, the computation can be performed by a singlephotonic processor serially over time, e.g., by performing eachmatrix-vector product one at a time and combining the results digitallyafter performing all of the individual matrix-vector multiplications todetermine the result of the matrix-matrix product (e.g., by storing theresults in an appropriate memory configuration).

The concept above can be generalized into computing a product (e.g., adot product) between two multidimensional tensors. The general algorithmis as follows and may be performed, at least in part, by a processorsuch as the processor 1-111: (1) Take a matrix slice of the firsttensor; (2) Take a vector slice of the second tensor; (3) Perform amatrix-vector product, using the photonic processor, between the matrixslice in step 1 and the vector slice in step 2, resulting in an outputvector; (4) Iterate over the tensor indices from which the matrix slice(from step 1) was obtained and the tensor indices from which the vectorslice (from step 2) was obtained. It should be noted that when takingthe matrix slice and the vector slice (steps 1 and 2), multiple indicescan be combined into one. For example, a matrix can be vectorized bystacking all the columns into a single column vector, and in general atensor can be matricized by stacking all the matrices into a singlematrix. Since all the operations are fully linear, they are again can behighly parallelized where each of a plurality of photonic processor doesnot need to know whether the other photonic processors have completedtheir jobs.

By way of a non-limiting example, consider the multiplication betweentwo three-dimensional tensors C_(ijlm)=Σ_(k)A_(ijk)B_(klm). Thepseudocode based on the prescription above is as follows:

(1) Take a matrix slice: A_(i)←A[i, :, :];

(2) Take a vector slice: {right arrow over (b_(lm))}←B[:, l, m];

(3) Compute {right arrow over (c_(ιlm))}=A_(i){right arrow over(b_(lm))}, where C[i, :, l, m]←{right arrow over (c_(ιlm))}; and

(4) Iterate over the indices i, l, and m to reconstruct thefour-dimensional tensor C_(ijlm), where the values of all elementsindexed by j is fully determined with a single matrix-vectormultiplication.

The inventors have further recognized and appreciated that the size ofthe matrices/vectors to be multiplied can be larger than the number ofmodes supported by the photonic processor. For example, a convolutionoperation in a convolutional neural network architecture may use only afew parameters to define a filter, but may consist of a number ofmatrix-matrix multiplications between the filter and different patchesof the data. Combining the different matrix-matrix multiplicationsresult in two input matrices that are larger than the size of theoriginal filter matrix or data matrix.

The inventors have devised a method of performing matrix operationsusing the photonic processor when the matrices to be multiplied arelarger than the size/the number of modes possessed by the photonicprocessor being used to perform the calculation. In some embodiments,the method involves using memory to store intermediate informationduring the calculation. The final calculation result is computed byprocessing the intermediate information. For example, as illustrated inFIG. 1-17, consider the multiplication 1-1700 between an I×J matrix Aand a J×K matrix B to give a new matrix C=AB, which has I×K elements,using an n×n photonic processing system with n≤I,J, K. In FIG. 1-17, theshaded elements simply illustrate that the element 1-1701 of matrix C iscalculated using the elements of row 1-1703 of matrix A and column1-1705 of matrix B. The method illustrated by FIGS. 1-17 and 1-18 is asfollows:

Construct n×n submatrix blocks of within matrices A and B. Label theblocks by the parenthesis superscript A^((ij)) and B^((jk)), where i∈{1,. . . , ceil(I/n)}, j∈{1, . . . , ceil(J/n)}, and k∈{1, . . . ,ceil(K/n)}. When the values of I, J, or K are not divisible by n, thematrices may be padded with zeros such that the new matrix hasdimensions that are divisible by n—hence the ceil function in theindexing of i, j, and k. In the example multiplication 1-1800illustrated in FIG. 1-18, matrix A is split into six n×n submatrixblocks 1-1803 and matrix B is split into three n×n submatrix blocks1-1805, resulting in a resulting matrix C that is comprised of two n×nsubmatrix blocks 1-1801.

To compute the n×n submatrix block C^((ik)) within matrix C, perform themultiplications C^((ik))=Σ_(j=1) ^(ceil(J/n)) A^((ij))B^((jk)) in thephotonic processor by, for example:

(1) Controlling the photonic processor to implement the submatrixA^((ij)) (e.g., one of the submatrices 1-1803);

(2) Encoding optical signals with the column vectors of one of thesubmatrices B^((jk)) (e.g., one of the submatrices 1-1805) andpropagating the signals through the photonic processor;

(3) Storing the intermediate results of each matrix-vectormultiplication in memory;

(4) Iterating over the values of j, repeating steps (a)-(c); and

(5) Computing the final submatrix C^((ik)) (e.g., one of the submatrices1-1801) by combining the intermediate results with digital electronics,e.g., a processor.

As described above and shown in FIG. 1-17 and FIG. 1-18, the method mayinclude expressing the matrix multiplication using parentheses indexnotation and performing the operation of the matrix-matrixmultiplication using the parentheses superscript indices instead of thesubscript indices, which are used to describe the matrix elements inthis disclosure. These parentheses superscript indices correspond to then×n block of submatrix. In some embodiments, the method can begeneralized to tensor-tensor multiplications by breaking up themultidimensional arrays into n×n submatrix block slices, for example, bycombining this method with the tensor-tensor multiplication describedabove.

In some embodiments, an advantage of processing blocks of submatricesusing a photonic processor with fewer number of modes is that itprovides versatility with regards to the shape of the matrices beingmultiplied. For example, in a case where I>>J, performing singular valuedecompositions will produce a first unitary matrix of size I², a secondunitary matrix of size J², and a diagonal matrix with J parameters. Thehardware requirements of storing or processing I² matrix elements, whichare much larger than the number of elements of the original matrix, canbe too large for the number of optical modes included in someembodiments of the photonic processor. By processing submatrices ratherthan the entire matrix all at once, any size matrices may be multipliedwithout imposing limitations based on the number of modes of thephotonic processor.

In some embodiments, the submatrices of B are further vectorized. Forexample, the matrix A may be first padded to a [(n·┌I/n┐)×(n·┌J/n┐)]matrix and then partitioned into a [┌I/n┐×┌J/n┐] grid of submatrices(each of size [n×n]) and A^((ij)) is the [n×n] submatrix in the i^(th)row and j^(th) column of this grid, B has been first padded to a[(n·┌J/n┐)×K] matrix and then partitioned into a [┌J/n┐×1] grid ofsubmatrices (each of size [n×K]) and B^((j)) is the [n×K] submatrix inthe j^(th) row of this grid, and C has been first padded to a[(n·┌J/n┐)×K] matrix and then partitioned into a [┌I/n┐×1] grid ofsubmatrices (each of size [n×K]) and C^((i)) is the [n×K] submatrix inthe i^(th) row of this grid. In this vectorized form, the computation isdenoted by: C^((i))=Σ_(j=1) ^(┌J/n┐) A^((ij))B^((j)).

Using the above vectorization process, a photonic processor can computeany GEMM by loading (┌I/n┐·┌J/n┐) different matrices into the photonicarray and, for each loaded matrix, propagating K different vectorsthrough the photonic array. This yields ┌I/n┐·┌J/n┐·K output vectors(each comprised of n elements), a subset of which may be added togetherto yield the desired [I×K] output matrix, as defined by the equationabove.

K. Precision of the Computation

The inventors have recognized and appreciated that the photonicprocessor 1-103, and/or any other photonic processor according to otherembodiments described in the present disclosure, is an instance ofanalog computer and, as most data in this information age are stored ina digital representation, the digital precision of the computationperformed by the photonic processor is important to quantify. In someembodiments, the photonic processor according to some embodimentsperforms a matrix-vector product: {right arrow over (y)}=M{right arrowover (x)}, where {right arrow over (x)} is the input vector, M is an n×nmatrix, and {right arrow over (y)} is the output vector. In indexnotation, this multiplication is written as y_(i)=Σ_(j=1)^(n)M_(ij)x_(j) which is the multiplication between n elements of M_(ij)(iterate over j) and n elements of x_(j) (iterate over j) and thensumming the results altogether. As the photonic processor is a physicalanalog system, in some embodiments the elements M_(ij) and x_(j) arerepresented with a fixed point number representation. Within thisrepresentation, if M_(ij)∈{0,1}^(m) ¹ is an m₁-bit number andx_(j)∈{0,1}^(m) ² is an m₂-bit number, then a total of m₁+m₂+log₂ (n)bits are used to fully represent the resulting vector element y_(i). Ingeneral, the number of bits used to represent the result of amatrix-vector product is larger than the number of bits required torepresent the inputs of the operation. If the analog-to-digitalconverter (ADC) used is unable to read out the output vector at fullprecision, then the output vector elements may be rounded to theprecision of the ADC.

The inventors have recognized and appreciated that constructing an ADCwith a high bit-precision at bandwidths that correspond to the rate atwhich input vectors in the form of optical signals are sent through thephotonic processing system can be difficult to achieve. Therefore, insome embodiments, the bit precision of the ADC may limit the bitprecision at which the matrix elements M_(ij) and the vector elementx_(j) are represented (if a fully precise computation is desired).Accordingly, the inventors have devised a method of obtaining an outputvector at its full precision, which can be arbitrarily high, bycomputing partial products and sums. For the sake of clarity, it will beassumed that the number of bits needed to represent either M_(ij) orx_(j) is the same, i.e. m₁=m₂=m. However, this assumption however canobviated in general and does not limit the scope of embodiments of thepresent disclosure.

The method, according to some embodiments, as a first act, includesdividing the bit-string representation of the matrix element M_(ij) andthe vector element x_(j) into d divisions with each division containingk=m/d bits. (If k is not an integer, zeros may be appended until m isdivisible by d.) As a result, the matrix element M_(ij)=M_(ij)^([0])2^(k(d-1))+M_(ij) ^([1])2^(k(d-2))+ . . . +M_(ij) ^([d-1])2⁰,where M_(ij) ^([a]) is the k-bit value of the a-th most significantk-bit string of M_(ij). In terms of bit string, one writes M_(ij)=M_(ij)^([0])M_(ij) ^([1]) . . . M_(ij) ^([d-1]). Similarly, one can alsoobtain x_(j)=x_(j) ^([0])2^(k(d-1))+x_(j) ^([1])2^(k(d-2))+ . . . +x_(j)^([d-1])2⁰, where the vector element x_(j)=x_(j) ^([0])x_(j) ^([1]) . .. x_(j) ^([d-1]) in terms of its bit string. The multiplicationy_(i)=Σ_(j)M_(ij)x_(j) can be broken down in terms of these divisionsas: y_(i)=Σ_(p=0) ^(2(d-1))((Σ_(a,b∈S) _(p) M_(ij) ^([a])x_(j)^([b]))2^(2k(d-1)-pk)), where the set S_(p) is the set of all integervalues of a and b, where a+b=p.

The method, as a second act, includes controlling the photonic processorto implement the matrix M_(ii) ^([a]) and propagating the input vectorx_(j) ^([b]), each of which is only k-bit precise, through the photonicprocessor in the form of encoded optical signals. This matrix-vectorproduct operation performs y_(i) ^([a,b])=Σ_(j)M_(ij) ^([a])x_(j)^([b]). The method includes, storing the output vector y_(i) ^([a,b])which is precise up to 2k+log₂ (n)bits.

The method further includes iterating over the different values of a, bwithin the set S_(p) and repeating the second act for each of thedifferent values of a, b and storing the intermediate results y_(i)^([a,b]).

As a third act, the method includes computing the final result Σ_(a,b∈S)_(p) Σ_(j)M_(ij) ^([a])x_(j) ^([b])=Σ_(a,b∈S) _(p) y_(i) ^([a,b]) bysumming over the different iterations of a and b with digitalelectronics, such as a processor.

The precision of the ADC used to capture a fully precise computationaccording to some embodiments of this method is only 2k+log₂(n) bits,which is fewer than the 2m+log₂ (n) bits of precision needed if thecomputation is done using only a single pass.

The inventors have further recognized and appreciated that embodimentsof the foregoing method can be generalized to operate on tensors. Aspreviously described, the photonic processing system can performtensor-tensor multiplications by using matrix slices and vector slicesof the two tensors. The method described above can be applied to thematrix slices and vector slices to obtain the output vector slice of theoutput tensor at full precision.

Some embodiments of the above method use the linearity of the elementaryrepresentation of the matrix. In the description above, the matrix isrepresented in terms of its Euclidean matrix space and the matrix-vectormultiplication is linear in this Euclidean space. In some embodiments,the matrix is represented in terms of the phases of the VBSs andtherefore the divisions may be performed on the bit strings representingthe phases, instead of the matrix elements directly. In someembodiments, when the map between the phases to the matrix elements is alinear map, then the relationship between the input parameters—thephases of the VBSs and the input vector elements in this case—and theoutput vector is linear. When this relationship is linear, the methoddescribed above is still applicable. However, in general, a nonlinearmap from the elementary representation of the matrix to the photonicrepresentation may be considered, according to some embodiments. Forexample, the bit-string division of the Euclidean space matrix elementsfrom their most-significant k-bit string to the least-significant k-bitstring may be used to produce a series of different matrices that aredecomposed to a phase representation and implementing using a photonicprocessor.

The divisions need not be performed on both the matrix elements and theinput vector elements simultaneously. In some embodiments, the photonicprocessor may propagate many input vectors for the same matrices. It maybe efficient to only perform the divisions on the input vectors and keepthe VBS controls at a set precision (e.g., full precision) because thedigital-to-analog converters (DACs) for the vector preparations mayoperate at a high bandwidth while the DACs for the VBSs may bequasi-static for multiple vectors. In general, including a DAC with ahigh bit precision at higher bandwidth is more difficult than designingone at a lower bandwidth. Thus, in some embodiments, the output vectorelements may be more precise than what is allowed by the ADC, but theADC will automatically perform some rounding to the output vector valueup to the bit precision allowed by the ADC.

L. Method of Manufacture

Embodiments of the photonic processing system may be manufactured usingconventional semiconductor manufacturing techniques. For example,waveguides and phase shifters may be formed in a substrate usingconventional deposition, masking, etching, and doping techniques.

FIG. 1-19 illustrates an example method 1-1900 of manufacturing aphotonic processing system, according to some embodiments. At act1-1901, the method 1-1900 includes forming an optical encoder using,e.g., conventional techniques. For example, a plurality of waveguidesand modulators may be formed in a semiconductor substrate. The opticalencoder may include one or more phase and/or amplitude modulators asdescribed elsewhere in this application.

At act 1-1903, the method 1-1900 includes forming a photonic processorand optically connecting the photonic processor to the optical encoder.In some embodiments, the photonic processor is formed in the samesubstrate as the optical encoder and the optical connections are madeusing waveguides formed in the substrate. In other embodiments, thephotonic processor is formed in a separate substrate from the substrateof the optical encoder and the optical connection is made using opticalfiber.

At act, 1-1905, the method 1-1900 include forming an optical receiverand optically connecting the optical receiver to the photonic processor.In some embodiments, the optical receiver is formed in the samesubstrate as the photonic processor and the optical connections are madeusing waveguides formed in the substrate. In other embodiments, theoptical receiver is formed in a separate substrate from the substrate ofthe photonic processor and the optical connection is made using opticalfiber.

FIG. 1-20 illustrates an example method 1-2000 of forming a photonicprocessor, as shown in act 1-1903 of FIG. 1-19. At act 1-2001, themethod 1-2000 includes forming a first optical matrix implementation,e.g., in a semiconductor substrate. The first optical matriximplementation may include an array of interconnected VBSs, as describedin the various embodiments above.

At act 1-2003, the method 1-2000 include forming a second optical matriximplementation and connecting the second optical matrix implementationto the first optical matrix implementation. The second optical matriximplementation may include one or more optical components that arecapable of controlling the intensity and phase of each optical signalreceived from the first optical matrix implementation, as described inthe various embodiments above. The connections between the first andsecond optical matrix implementation may include waveguides formed inthe substrate.

At act 1-2005, the method 1-2000 includes forming a third optical matriximplementation and connecting the third optical matrix implementation tothe second optical matrix implementation. The third optical matriximplementation may include an array of interconnected VBSs, as describedin the various embodiments above. The connections between the second andthird optical matrix implementation may include waveguides formed in thesubstrate.

In any of the above acts, the components of the photonic processor maybe formed in a same layer of the semiconductor substrate or in differentlayers of the semiconductor substrate.

M. Method of Use

FIG. 1-21 illustrates a method 1-2100 of performing optical processing,according to some embodiments. At act 1-2101, the method 1-2100 includesencoding a bit string into optical signals. In some embodiments, thismay be performed using a controller and optical encoder, as described inconnection with various embodiments of this application. For example, acomplex number may be encoded into the intensity and phase of an opticalsignal.

At act 1-2103, the method 1-2100 includes controlling a photonicprocessor to implement a first matrix. As described above, this may beaccomplished by having a controller perform an SVD on the matrix andbreak the matrix into three separate matrix components that areimplemented using separate portions of a photonic processor. Thephotonic processor may include a plurality of interconnected VBSs thatcontrol how the various modes of the photonic processor are mixedtogether to coherently interfere the optical signals when they arepropagated through the photonic processor.

At act 1-2105, the method 1-2100 includes propagating the opticalsignals though the optical processor such that the optical signalscoherently interfere with one another in a way that implements thedesired matrix, as described above.

At act, 1-2107, the method 1-2100 includes detecting output opticalsignals from the photonic processor using an optical receiver. Asdiscussed above, the detection may use phase-sensitive orphase-insensitive detectors. In some embodiments, the detection resultsare used to determine a new input bit string to be encoded andpropagated through the system. In this way, multiple calculations may beperformed in serial where at least one calculation is based on theresults of a previous calculation result.

II. Training Algorithm

The inventors have recognized and appreciated that for many matrix-baseddifferentiable program (e.g., neural network or latent-variablegraphical model) techniques, the bulk of the computational complexitylies in matrix-matrix products that are computed as layers of the modelare traversed. The complexity of a matrix-matrix product is O(IJK),where the two matrices have dimension I-by-J and J-by-K. Moreover, thesematrix-matrix products are performed in both the training stage and theevaluation stage of the model.

A deep neural network (i.e., a neural network with more than one hiddenlayer) is an example of a type of matrix-based differentiable programthat may employ some of the techniques described herein. However, itshould be appreciated that the techniques described herein forperforming parallel processing may be used with other types ofmatrix-based differentiable programs including, but not limited to,Bayesian networks, Trellis decoders, topic models, and Hidden MarkovModels (HMMs).

The success of deep learning is in large part due to the development ofbackpropagation techniques that allow for training the weight matricesof the neural network. In conventional backpropagation techniques, anerror from a loss function is propagated backwards through individualweight matrix components using the chain rule of calculus.Backpropagation techniques compute the gradients of the elements in theweight matrix, which are then used to determine an update to the weightmatrix using an optimization algorithm, such as stochastic gradientdescent (SGD), AdaGrad, RMSProp, Adam, or any other gradient-basedoptimization algorithm. Successive application of this procedure is usedto determine the final weight matrix that minimizes the loss function.

The inventors have recognized and appreciated that an optical processorof the type described herein enables the performance of a gradientcomputation by recasting the weight matrix into an alternative parameterspace, referred to herein as a “phase space” or “angularrepresentation.” Specifically, in some embodiments, a weight matrix isreparameterized as a composition of unitary transfer matrices, such asGivens rotation matrices. In such a reparameterization, training theneural network includes adjusting the angular parameters of the unitarytransfer matrices. In this reparameterization, the gradient of a singlerotation angle is decoupled from the other rotations, allowing parallelcomputation of gradients. This parallelization results in acomputational speedup relative to conventional serial gradientdetermination techniques in terms of the number of computation stepsneeded.

An example photonic processing system that may be used to implement thebackpropagation techniques described herein is provided above. The phasespace parameters of the reparameterized weight matrix may be encodedinto phase shifters or variable beam splitters of the photonicprocessing system to implement the weight matrix. Encoding the weightmatrix into the phase shifters or variable beam splitters may be usedfor both the training and evaluation stages of the neural network. Whilethe backpropagation procedure is described in connection with theparticular system described below, it should be understood thatembodiments are not limited to the particular details of the photonicprocessing system described in the present disclosure.

As described above, in some embodiments, photonics processing system 100may be used to implement aspects of a neural network or othermatrix-based differentiable program that may be trained using abackpropagation technique.

An example backpropagation technique 2-100 for updating a matrix ofvalues in a Euclidean vector space (e.g., a weight matrix for a layer ofa neural network) for a differentiable program (e.g., a neural networkor latent variable graphical model) is shown in FIG. 2-1.

At act 2-101, a matrix of values in a Euclidean vector space (e.g., aweight matrix for a layer of a neural network) may be represented as anangular representation by, for example, configuring components ofphotonics processing system 100 to represent the matrix of values. Afterthe matrix is represented in the angular representation, the process2-100 proceeds to act 2-102, where training data (e.g., a set of inputtraining vectors and associated labeled outputs) is processed to computean error vector by assessing a performance measure of the model. Process2-100 then proceeds to act 2-103, where at least some gradients ofparameters of the angular representation needed for backpropagation aredetermined in parallel. For example, as discussed in more detail below,the techniques described herein enable gradients for an entire column ofparameters to be determined simultaneously, significantly speeding upthe amount of time needed to perform backpropagation as compared toevaluating the gradient with respect to each angular rotationindividually. Process 2-100 then proceeds to act 2-104, where the matrixof values in the Euclidean vector space (e.g., the weight matrix valuesfor a layer of a neural network) is updated by updating the angularrepresentation using the determined gradients. A further description ofeach of the acts illustrated in process 2-100 of FIG. 2-1 is providedbelow.

FIG. 2-2 illustrates a flowchart of how act 2-101 shown in FIG. 2-1 maybe performed in accordance with some embodiments. At act 2-201, acontroller (e.g., controller 107) may receive a weight matrix for alayer of a neural network. At act 2-202, the weight matrix may bedecomposed into a first unitary matrix, V, a second unitary matrix, U,and a diagonal matrix of signed singular values, Σ, such that the weightmatrix W, is defined as:

W=V ^(T) ΣU,

where U is an m×m unitary matrix, V is an n×n unitary matrix, Σ is ann×m diagonal matrix with signed singular values, and the superscript “T”indicates the transpose of a matrix. In some embodiments, the weightmatrix W is first partitioned into tiles, each of which is decomposedinto the triple product of such matrices. The weight matrix, W, may be aconventional weight matrix as is known in the field of neural networks.

In some embodiments, the weight matrix decomposed into phase space is apre-specified weight matrix, such as those provided by a randominitialization procedure or by employing a partially trained weightmatrix. If there is no partially-specified weight matrix to use forinitialization of the backpropagation routine, then the decomposition inact 2-202 may be skipped and instead, the parameters of the angularrepresentation (e.g., singular values and parameters of the unitary ororthogonal decomposition) may be initialized by, for example, randomlysampling the phase space parameters from a particular distribution. Inother embodiments, a predetermined initial set of singular values andangular parameters may be used.

At act 2-203, the two unitary matrices U and V may be represented ascompositions of a first and second set of unitary transfer matrices,respectively. For example, when matrices U and V are orthogonalmatrices, they may be transformed in act 2-203 into a series ofreal-valued Givens rotation matrices or Householder reflectors, anexample of which is described above in Section V.

At act 2-204, a photonics-based processor may be configured based on thedecomposed weight matrix to implement the unitary transfer matrices. Forexample, as described above, a first set of components of thephotonics-based processor may be configured based on the first set ofunitary transfer matrices, a second set of components of thephotonics-based processor may be configured based on the diagonal matrixof signed singular values, and a third set of components of thephotonics-based processor may be configured based on the second set ofunitary transfer matrices. Although the processes described herein arewith respect to implementing the backpropagation technique using aphotonics-based processor, it should be appreciated that thebackpropagation technique may be implemented using other computingarchitectures that provide parallel processing capabilities, andembodiments are not limited in this respect.

Returning to the process 2-100 in FIG. 2-1, act 2-102 is directed toprocessing training data to compute an error vector after the weightmatrix has been represented as an angular representation, for example,using a photonics-based processor as described above. FIG. 2-3 shows aflowchart of implementation details for performing act 2-102 inaccordance with some embodiments. Prior to processing training datausing the techniques described herein, the training data may dividedinto batches. Training data may take any form. In some embodiments, thedata may be divided into batches in the same way as is done for someconventional mini-batch stochastic gradient descent (SGD) techniques.The gradients computed with this procedure can be used for anyoptimization algorithm, including but not limited to SGD, AdaGrad, Adam,RMSProp, or any other gradient-based optimization algorithm.

In the process shown in FIG. 2-3, each vector in a particular batch oftraining data may be passed through the photonics processor and a valueof a loss function may be computed for that vector. At act 2-301, aninput training vector from a particular batch of training data isreceived. At act 2-302, the input training vector is converted intophotonic signals, for example, by encoding the vector using opticalpulses that have amplitudes and phases corresponding to the inputtraining vector values, as described above. At act 2-303, the photonicsignals corresponding to the input training vector are provided as inputto a photonics processor (e.g., photonics processor 103), which has beenconfigured to implement (e.g., using an array of configurable phaseshifters and beam splitters) a weight matrix, as described above, toproduce an output vector of pulses. The optical intensity of the pulsesoutput from the photonics processor may be detected using, for example,homodyne detection, as described above in connection with FIGS. 9 and 10to produce a decoded output vector. At act 2-304, the value of a lossfunction (also known as a cost function or error metric) is computed forthe input training vector. The process of acts 2-301 to 2-304 thenrepeats until all input training vectors in the particular batch havebeen processed and a corresponding value of the loss function has beendetermined. At act 2-305, the total loss is computed by aggregatingthese losses from each input training vector, e.g., this aggregation maytake the form of an average.

Returning to the process 2-100 in FIG. 2-1, act 2-103 is directed tocomputing in parallel gradients for the parameters of the angularrepresentation (e.g., the values of the weights that have beenimplemented using the components of the photonics processor as Givensrotations). The gradients may be calculated based on the computed errorvectors, input data vectors (e.g., from a batch of training data), andthe weight matrix implemented using the photonics processor. FIG. 2-4show a flowchart of a process for performing act 2-103 in accordancewith some embodiments. At act 2-401, for the k-th set of Givensrotations, G^((k)) in the decomposition (e.g., a column of thedecomposed matrix—referred to herein as “derivative column k”), a blockdiagonal derivative matrix containing the derivatives with respect toeach angle in the k-th set is computed. At act 2-402, a product of theerror vector determined in act 2-102 with all of the unitary transfermatrices between the derivative column k and the output is computed(hereby referred to as a “partial backward pass”). At act 2-403, aproduct of the input data vector with all unitary transfer matricesstarting from the input up to and including the derivative column k iscomputed (hereby referred to as a “partial forward pass”) At act 2-404,the inner products between successive pairs of elements output from acts2-402 and 2-403 are computed to determine the gradients for thederivative column k. The inner products between successive pairs ofelements may be computed as

${\frac{\partial W^{(l)}}{\partial\theta_{ij}^{(k)}} = {{x_{i}\delta_{i}} + {x_{j}\delta_{j}}}},$

where superscript (k) represents the k-th column of photonic elementsand i, j represent the i-th and j-th photonic mode that are beingcoupled by the unitary transfer matrix with parameter θ_(ij) ^((k)), xis the output of the partial forward pass and δ is the output of thepartial backward pass. In some embodiments, an offset is applied beforethe successive pairing of the outputs (e.g., output pairs could be (1,2), (3, 4), etc. rather than (0, 1), (2, 3)). The determined gradientsmay then be used as appropriate for a particular chosen optimizationalgorithm (e.g., SGD) that is being used for training.

Example pseudocode for implementing the backpropagation technique on aphotonic processor having the left-to-right topology shown in FIGS. 1-4in accordance with some embodiments is provided below:

Initialize two lists for intermediate propagation results, x′, δ′.

For each column of MZI, staring with the last column and going to thefirst column:

-   -   Rotate the angles in the column to correspond to a derivative        matrix    -   Propagate the input data vector through the photonics processor    -   Store the result in x′    -   Make the current column transparent

Column-by-column, progressively build up the transpose matrix. For eachnew column:

-   -   Propagate the error vector through the photonics processor    -   Store the result in δ′

For each x′[i], δ′[i]

-   -   Compute the inner products between successive pairs, with the        result being the gradients for the angles in the ith column of        MZI

According to some embodiments, rather than adjusting the weights of aweight matrix via gradient descent as is done in some conventionalbackpropagation techniques, the parameters of angular representation(e.g., the singular values of the matrix Σ and the Givens rotationangles of the decomposed orthogonal matrices U and V) are adjusted. Tofurther demonstrate how backpropagation in the reparameterized spaceworks according to some embodiments, what follows is a comparison ofbackpropagation within a single layer of a neural network usingconventional techniques versus the method according to some embodimentsof the present disclosure.

A loss function, E, measures the performance of the model on aparticular task. In some conventional stochastic gradient descentalgorithms, the weight matrix is adjusted iteratively such that theweight matrix at time t+1 is defined as a function of the weight matrixat time t and a derivative of the loss function with respect to theweights of the weight matrix is as follows:

${{w_{ab}\left( {t + 1} \right)} = {{w_{ab}(t)} - {\eta\frac{\partial E}{\partial{w_{ab}(t)}}}}},$

where η is the learning rate and (a,b) represent the a-th row and b-thcolumn entry of the weight matrix W, respectively. When this iterativeprocess is recast using the decomposed weight matrix, the weights w_(ab)are functions of the singular values σ_(i) of the matrix Σ and therotation angles θ_(ij) of the orthogonal matrices U and V. Thus theiterative adjustments of the backpropagation algorithm become:

${\theta_{ij}^{(k)}\left( {t + 1} \right)} = {{\theta_{ij}^{(k)}(t)} - {\eta\frac{\partial E}{\partial{\theta_{ij}^{(k)}(t)}}}}$and${\sigma_{i}\left( {t + 1} \right)} = {{\sigma_{i}(t)} - {\eta\frac{\partial E}{\partial{\sigma_{i}(t)}}}}$

To perform iterative adjustments to the singular values and rotationangles, the derivatives of the loss function must be obtained. Beforedescribing how this can be achieved in a system such as the photonicprocessing system 100, a description is first provided forbackpropagation based on iteratively adjusting the weights of the weightmatrix. In this situation, the output result measured by the system fora single layer of the neural network is expressed as an output vectory_(i)=f((Wx)_(i)+b_(i)), where W is the weight matrix, x is the datavector input into the layer, b is a vector of biases, and f is anonlinear function. The chain rule of calculus is applied to compute thegradient of the loss function with respect to any of the parameterswithin the weight matrix (where for convenience of representation, thedefinition z_(i)=(Wx)_(i)+b_(i) is used):

$\frac{\partial E}{\partial w_{ab}} = {\sum\limits_{ij}{\frac{\partial E}{\partial y_{i}}\frac{\partial y_{i}}{\partial z_{j}}\frac{\partial z_{j}}{\partial w_{ab}}}}$

Computing the derivative of z with respect to w_(ab) results in:

$z_{i} = {{({Wx})_{i} + b_{i}} = {b_{i} + {\sum\limits_{j}{W_{ij}x_{j}}}}}$$\frac{\partial z_{j}}{\partial w_{ab}} = {\delta_{ja}x_{b}}$

Using this fact, the sum representing the gradient of the loss functioncan then be written as:

$\frac{\partial E}{\partial w_{ab}} = {\sum\limits_{i}{\frac{\partial E}{\partial y_{i}}\frac{\partial y_{i}}{\partial z_{a}}x_{b}}}$

The first sum is defined as the error vector e and x is the inputvector, resulting in the following expression:

$\frac{\partial E}{\partial w_{ab}} = {e_{a}x_{b}}$

Using the above equations from conventional backpropagation techniques,the equations can be extended to the case of weight matrices decomposedinto a singular value matrix and unitary transfer matrices. Using thefact that the weight matrix is a function of rotation angles, the chainrule can be used to write:

$\begin{matrix}{\frac{\partial E}{\partial\theta_{ij}^{(k)}} = {\sum\limits_{ab}{\frac{\partial E}{\partial w_{ab}}\frac{\partial w_{ab}}{\partial\theta_{ij}^{(k)}}}}} \\{= {\sum\limits_{ab}{e_{a}x_{b}\frac{\partial w_{ab}}{\partial\theta_{ij}^{(k)}}}}} \\{= {{\overset{\rightarrow}{e}}^{T}\frac{\partial W}{\partial\theta_{ij}^{(k)}}\overset{\rightarrow}{x}}}\end{matrix}$

Thus, the backpropagation in phase space involves the same components asin conventional backpropagation (the error vector and the input data),with the addition of a term that is the derivative of the weight matrixwith respect to the rotation angles of the unitary transfer matrices.

To determine the derivative of the weight matrix with respect to therotation angles of the unitary transfer matrices, it is noted that thederivative of a single Givens rotation matrix has the following form:

$\begin{matrix}{\frac{\partial G_{ij}^{(k)}}{\partial\theta_{ij}^{(k)}} = {\frac{\partial}{\partial\theta_{ij}^{(k)}}\begin{pmatrix}1 & 0 & \ldots & \; & \; & \; \\0 & \ddots & \; & \; & \; & \; \\\vdots & \; & {\cos\mspace{14mu}\theta_{ij}^{(k)}} & {{- \sin}\mspace{14mu}\theta_{ij}^{(k)}} & \; & \; \\\; & \; & {\sin\mspace{14mu}\theta_{ij}^{(k)}} & {\cos\mspace{14mu}\theta_{ij}^{(k)}} & \; & \vdots \\\; & \; & \; & \; & \ddots & 0 \\\; & \; & \; & \ldots & 0 & 1\end{pmatrix}}} \\{= \begin{pmatrix}0 & 0 & \ldots & \; & \; & \; \\0 & \ddots & \; & \; & \; & \; \\\vdots & \; & {{- \sin}\mspace{14mu}\theta_{ij}^{(k)}} & {{- \cos}\mspace{14mu}\theta_{ij}^{(k)}} & \; & \; \\\; & \; & {\cos\mspace{14mu}\theta_{ij}^{(k)}} & {{- \sin}\mspace{14mu}\theta_{ij}^{(k)}} & \; & \vdots \\\; & \; & \; & \; & \ddots & 0 \\\; & \; & \; & \ldots & 0 & 0\end{pmatrix}}\end{matrix}$

As can be seen, the derivative for any entry of the Givens rotationmatrix that is not in the i-th row and j-th column is zero. Thus, allderivatives for angles inside G^((k)) may be grouped into a singlematrix. To compute the derivative with respect to all floor

$\left( \frac{n}{2} \right)$

angles inside a column of unitary transfer matrices may, in someembodiments, be accomplished using a two-step process, as describedabove. First, the error vector is propagated through the decomposedmatrix from the right (output) up to the current set of rotations beingdifferentiated (partial backward pass). Second, the input vector ispropagated from the left (input) up to the current set of rotations(partial forward pass), and then the derivative matrix is applied.

In some embodiments, the derivation for the singular values is achievedusing a similar process. The derivative with respect to a singular valueσ_(i) results in the element Σ′_(ii) to 1 and all other Σ′_(jj) to 0.Therefore, all of the derivatives for the singular values may becalculated together. In some embodiments, this may be done bypropagating the error vector from the left (partial forward pass) andpropagating the input vector from the right (partial backward pass),then computing the Hadamard product from the outputs of the forward passand the backward pass.

In the implementation of act 2-103 described in FIG. 2-4, all of theangles in a column k are rotated by π/2 in order to compute the gradientterm for that column. In some embodiments, this rotation is notperformed. Consider the matrix for a single MZI:

$\begin{pmatrix}{\cos\mspace{14mu}\theta} & {{- \sin}\mspace{20mu}\theta} \\{\sin\mspace{14mu}\theta} & {\cos\mspace{14mu}\theta}\end{pmatrix}\quad$

Taking the derivative with respect to θ yields

$\begin{pmatrix}{{- \sin}\mspace{14mu}\theta} & {{- \cos}\mspace{14mu}\theta} \\{\cos\mspace{14mu}\theta} & {{- \sin}\mspace{14mu}\theta}\end{pmatrix}\quad$

While this matrix corresponds to adding π/2 to θ, it also corresponds toswapping the columns of the original matrix and negating one of them. Inmathematical notation, this means

$\begin{pmatrix}{{- \sin}\mspace{14mu}\theta} & {{- \cos}\mspace{14mu}\theta} \\{\cos\mspace{14mu}\theta} & {{- \sin}\mspace{14mu}\theta}\end{pmatrix} = {\begin{pmatrix}0 & 1 \\{- 1} & 0\end{pmatrix}\begin{pmatrix}{\cos\mspace{14mu}\theta} & {{- \sin}\mspace{14mu}\theta} \\{\sin\mspace{14mu}\theta} & {\cos\mspace{14mu}\theta}\end{pmatrix}}$

Rather than rotating the angle of each MZI in a column by π/2 and thencomputing the inner products between successive pairs of elements outputfrom acts 2-402 and 2-403 as described above (e.g., x₁δ₁+x₂δ₂), todetermine the gradients for a column of the decomposed unitary matrix,in some embodiments, the angles are not rotated by π/2 and instead therelation x₁δ₂−x₂δ₁ is calculated to obtain the same gradients. In someembodiments, where the size of the matrix W (n×m) matches the size ofthe photonics processor with matrix U of size n×n and matrix V of sizem×m, acts 2-401-2-404 allow the controller to obtain floor

$\left( \frac{n}{2} \right)$

gradients for a unitary/orthogonal matrix of size n×n. Consequently, onhardware such the photonic processing system 100 described above, whereeach matrix multiplication can be computed in O(1) operations, theoverall backpropagation procedure may be completed in O(n+m) operationswhen the photonic processor is of sufficient size to represent the fullmatrix. When the photonics processor is not of sufficient size torepresent the full matrix, the matrix may be partitioned into tiles, asdescribed above. Consider a photonic processor of size N. If the task isto multiply a matrix of size I×J by a vector of size J, a singlematrix-vector product will have complexity O(IJ/N²) (assuming that bothI and J are divisible by N), because each dimension of the matrix mustbe partitioned into matrices of size N, loaded into the processor, andused to compute a partial result. For a batch of K vectors (e.g., asecond matrix of size J×K), the complexity is O(IJK/N²) for thematrix-vector product.

An embodiment of a photonic processor, as described above, with noptical modes naturally computes a matrix-vector product between amatrix of size [n×n] and an n-element vector. This is equivalentlyexpressed as a matrix-matrix product between matrices of sizes [n×n] and[n×1]. Furthermore, a sequence of K matrix-vector product operationswith K different input vectors and a single, repeated input matrix canbe expressed as the computation of a matrix-matrix product betweenmatrices of size [n×n] and[n×K]. But the applications and algorithmsdescribed herein often involve the computation of general matrix-matrixmultiplication (GEMM) between matrices of arbitrary size; i.e., thecomputation

${c_{ik} = {\sum\limits_{j = 1}^{j}\;{{a_{ij} \cdot b_{jk}}\mspace{14mu}{\forall{i \in \left\lbrack {1,I} \right\rbrack}}}}},{k \in \left\lbrack {1,K} \right\rbrack}$

Where a_(ij) is the element in the i^(th) row and j^(th) column of an[I×J] matrix A, b_(jk) is the j^(th) row and k^(th) column of a [J×K]matrix B and c_(ik) is the element in the i^(th) row and k^(th) columnof the [I×K] matrix C=AB. Due to the recursive nature of thiscomputation, this can be equivalently expressed as:

${C_{i} = {\sum\limits_{j = 1}^{\lceil{J\text{/}n}\rceil}\;{A_{ij}B_{j}\mspace{14mu}{\forall{i \in \left\lbrack {1,I} \right\rbrack}}}}},{k \in \left\lbrack {1,K} \right\rbrack}$

Where A has been first padded to a

$\left\lbrack {{n \cdot \left\lceil \frac{I}{n} \right\rceil} \times \left( {n \cdot \left\lceil \frac{J}{n} \right\rceil} \right)} \right\rbrack$

matrix and then partitioned into a [┌I/n┐×┌J/n┐] grid of submatrices(each of size [n×n]) and A_(ij) is the [n×n] submatrix in the i^(th) rowand j^(th) column of this grid, B has been first padded to a[(n·┌J/n┐×K)] matrix and then partitioned into a [┌J/n┐×1] grid ofsubmatrices (each of size [n×K]) and B_(j) is the [n×K] submatrix in thej^(th) row of this grid, and C has been first padded to a [(n·┌J/n┐)×K]matrix and then partitioned into a [┌I/n┐×1] grid of submatrices (eachof size [n×K]) and C_(i) is the [n×K] submatrix in the i^(th) row ofthis grid.

Using this process, a photonic processor can compute any GEMM by loading(┌I/n┐·┌J/n┐) different matrices into the photonic array and, for eachloaded matrix, propagating k different vectors through the photonicarray. This yields ┌I/n┐·┌J/n┐·k output vectors (each comprised of nelements), a subset of which may be added together to yield the desired[I×K] output matrix, as defined by the equation above.

In the implementation of act 2-103 described in FIG. 2-4, aleft-to-right topology of the photonics processor for implementing thematrices was assumed, where the vector was input on the left of thearray of optical components and the output vector was provided on theright of the array of optical components. This topology requires thetranspose of the angular representation matrix to be calculated whenpropagating the error vector through the photonics processor. In someembodiments, the photonic processor is implemented using a folded-overtopology that arranges both the inputs and outputs on one side of thearray (e.g., the left side) of optical components. Such an architectureallows the use of a switch to decide which direction the light shouldpropagate—either from input to output or output to input. With thechoice of direction configurable on the fly, the propagation of theerror vector can be accomplished by first switching the direction to useoutput as input and then propagating the error vector through the array,which eliminates the need to negate phases (e.g., by rotating the angleof each photonic element in a column k by π/2) and transpose the columnswhen the gradients for column k are being determined, as describedabove.

Returning to the process 2-100 in FIG. 2-1, act 2-104 is directed toupdating the weight matrix by updating the parameters of the angularrepresentation based on the determined gradients. The process shown inFIG. 2-4, just described, is for computing the gradients of the angularparameters for a single set (e.g., column k) of gradients in thedecomposed unitary matrix based on a single input data exemplar. Inorder to update the weight matrix, the gradients for each of the sets(e.g., columns) need to be computed for each input data vector in abatch. FIG. 2-5 shows a flowchart of a process for computing all of thegradients needed to update the weight matrix in accordance with someembodiments. At act 2-501, the gradients for one set of multiple sets ofunitary transfer matrices (e.g., Givens rotations) is determined (e.g.,using the process shown in FIG. 2-4). At act 2-502, it is thendetermined whether there are additional sets of unitary transfermatrices (e.g., columns) for which gradients need to be calculated forthe current input data vector. If it is determined that there areadditional gradients to be calculated, the process returns to act 2-501,where a new set (e.g., column) is selected and gradients are calculatedfor the newly selected set. As noted below, in some computingarchitectures, all columns of the array may be read out simultaneously,such that the determination at act 2-502 is not needed. The processcontinues until it is determined at act 2-502 that gradients have beendetermined for all of the sets of unitary transfer matrices (e.g.,columns) in the decomposed unitary matrix. The process then proceeds toact 2-503, where it is determined whether there are more input datavectors to process in the batch of training data being processed. If itis determined that there are additional input data vectors, the processreturns to act 2-501, where a new input data vector from the batch isselected and gradients are calculated based on the newly selected inputdata vector. The process repeats until it is determined at act 2-503that all input data vectors in the batch of training data have beenprocessed. At act 2-504, the gradients determined in acts 2-501-2-503are averaged and the parameters of the angular representation (e.g., theangles of the Givens rotation matrices of the decomposition of theweight matrix) are updated based on the an update rule that makes use ofthe averaged gradients. As a non-limiting example, an update rule mayinclude scaling the averaged gradients by a learning rate or include“momentum” or other corrections for the history of parameter gradients.

As discussed briefly above, although the above example was applied to areal weight matrix in a single layer neural network, the results may begeneralized to networks with multiple layers and complex weightmatrices. In some embodiments, the neural network consists of multiplelayers (e.g., ≥50 layers in a deep neural network). To compute thegradient for a matrix of layer L, the input vector to that layer wouldbe the output of preceding layer L−1 and the error vector to that layerwould be the error backpropagated from the following layer L+1. Thevalue of the backpropagated error vector can be computed using the chainrule of multivariable calculus as before. Moreover, in some embodiments,complex U and V matrices (e.g., unitary matrices) may be used by addingan additional complex phase term to the Givens rotation matrix.

While the description above applies generally independent of thehardware architecture, certain hardware architectures provide moresignificant computation acceleration than others. In particular,implementation of the backpropagation techniques described herein on agraphical processing unit, a systolic matrix multiplier, a photonicprocessor (e.g., photonic processing system 100), or other hardwarearchitectures capable of parallel computations of the gradients arepreferred for the greatest gains compared to conventional approaches.

As described above, the photonic processing system 100 is configured toimplement any unitary transformation. A sequence of Givens rotations isan example of such a unitary transformation, and thus the photonicprocessing system 100 can be programmed to compute the transformationsin the decomposition above in O(1) time. As described above, the matrixmay be implemented by controlling a regular array of variablebeam-splitters (VBSs). The unitary matrices U and V^(T) may bedecomposed into a tiled array of VBS, each of which performs a 2-by-2orthogonal matrix operation (e.g., a Givens rotation). The diagonalmatrix Σ, along with the diagonal phase screen D_(U) and D_(V) (in theform of the diagonal matrix D_(U)ΣD_(V)), can be implemented in thephotonic processing system 100 by controlling the intensity and phase ofthe light pulses, as described above.

Each entry in the diagonal matrix Σ corresponds to the amplification orattenuation of each photonic mode. An entry with magnitude ≥1corresponds to amplification and an entry with magnitude ≤1 correspondsto attenuation, and a combination VBS and gain medium would allow foreither attenuation or amplification. For an n-by-n square matrix M, thenumber of optical modes needed to apply the diagonal matrix Σ is n.However, if the matrix M is not square, the number of optical modesneeded is equal to the smaller dimension.

As noted above, in some embodiments, the size of the photonic processoris the same as the size of the matrix M and input vector beingmultiplied. However, in practice, the size of the matrix M and the sizeof the photonic processor often differs. Consider a photonic processorof size N. If the task is to multiply a matrix of size I×J by a vectorof size J, a single matrix-vector product will have complexity O(IJ/N²)(assuming that both I and J are divisible by N), because each dimensionof the matrix must be partitioned into matrices of size N, loaded intothe processor, and used to compute a partial result. For a batch of Kvectors (e.g., a second matrix of size J×K), the complexity is O(IJK/N²)for the matrix-vector product.

The ability to work on small N-by-N matrix partitions can beadvantageous if the matrix is non-square, especially if either I>>J orJ>>I. Assuming a non-square matrix A, direct SVD of the matrix producesone I×I unitary matrix, one J×J unitary matrix, and one I×J diagonalmatrix. If either I>>J or J>>I, the number of parameters needed torepresent this decomposed matrices are much larger than the originalmatrix A.

However, if the matrix A is partitioned into multiple N×N squarematrices having smaller dimensions, SVD on these N×N matrices producestwo N×N unitary matrices and one N×N diagonal matrix. In this case, thenumber of parameters needed to represent the decomposed matrices isstill N²—equal to the size of the original matrix A, and the totalnon-square matrix can be decomposed with ≈IJ total parameters. Theapproximation becomes equality when IJ is divisible by N².

For a photonic processor having 2N+1 columns, the partial results ofbackpropagating the error vector for each column may be computed.Therefore, for a batch of K vectors, the complexity of backpropagationusing a photonic processor of size N is O(IJKI/N). By comparison, thecomputation of backpropagated errors using a matrix multiplicationalgorithm on a non-parallel processor (e.g., a CPU) would be O(IJK).

The description so far has focused on the use of a matrix within aneural network layer with an input vector data and a backpropagatederror vector. The inventors have recognized and appreciated that thedata in deep neural network computations are not necessarily vectors,but they are in general multidimensional tensors. Similarly, the weightvalues that describe the connection between the neurons are in generalalso multidimensional tensors. In some embodiments, the method describedabove can be directly applied if the weight tensor is sliced into matrixslices with each matrix slice being independent of one another.Therefore, singular value decomposition and the Givens-like rotationdecomposition can be performed to obtain a valid representation in termsof phases for a particular matrix slice. The same method of computingthe gradient of the phases can then be applied with the properarrangement of the input tensor data and the backpropagated error dataas well. The gradients for a specific matrix slice should be computedwith parts of the input and error data that would have contributed tothat particular matrix slice.

For concreteness, consider a general n-dimensional weight tensor w_(a) ₁_(a) ₂ _(a) ₃ _(. . . a) _(i) _(. . . a) _(n) . Choose two indices outof {a_(i)}_(i=1, . . . , n) that would constitute the matrix slice—saythat choice is labeled by indices a_(b) and a_(c)—and perform thedecomposition to obtain the phases θ_(ij) ^((k)) by computing ∂E/∂θ_(ij)^((k)). Importantly, b and c can be any value between 1 and n. Considernow a general k-dimensional input tensor x_(a) ₁ _(. . . a) _(i)_(. . . a) _(k) . For a valid tensor operation, it must be the case thatthe output of the operation of the weight tensor on this input tensorproduces an (n−k)-dimensional output tensor. It can therefore beconcluded that the backpropagated error tensor the weight tensor of thislayer is an (n−k)-dimensional tensor: e_(a) _(k+1) _(. . . a) _(i)_(. . . a) _(n) . Therefore, the gradient to be computed is

${{{\partial E}\text{/}{\partial\theta_{ij}^{(k)}}} = {\sum\limits_{a_{i}\ldots\; a_{n}}{e_{a_{1}\cdots\; a_{b}\cdots\; a_{n}}w_{a_{1}\cdots\; a_{b}\cdots\; a_{c}\cdots\; a_{n}}x_{a_{1}\cdots\; a_{c}\cdots\; a_{k}}}}},$

where, for simplicity (but not a necessary condition), the indices ofthe weight tensors have been ordered such that the first k indicesoperate on x, and the last (n-k) indices operate on the error e.

In other embodiments, it may be more convenient to perform higher-ordergeneralization of singular value decomposition such as the Tuckerdecomposition, where an arbitrary n-dimensional tensor can be decomposedas such: w_(a) ₁ _(. . . a) _(n) =Σ_(b) ₁ _(. . . b) _(n) g_(b) ₁_(. . . b) _(n) U_(a) ₁ _(b) ₁ ⁽¹⁾U_(a) ₂ _(b) ₂ ⁽²⁾ . . . U_(a) _(n)_(b) _(n) ^((n)), where each U_(a) _(i) _(b) _(i) ^((i)) is anorthogonal matrix that can be decomposed into its Givens rotation phasesand g_(b) ₁ . . . _(b) _(n) is an n-dimensional core tensor. In somecases, the core tensor can be chosen to be superdiagonal using a specialcase of the Tucker decomposition called CANDECOMP/PARAFAC (CP)decomposition. The Tucker decomposition can be made similar to the2-dimensional SVD form by multiplying the inverses (transposes orconjugate transposes) of some of the unitary matrices. For example, thedecomposition can be rewritten as w_(a) ₁ . . . _(a) _(n) =Σ_(b) ₁_(. . . b) _(n) (U^(T))_(b) ₁ _(a) ₁ ⁽¹⁾ . . . (U^(T))_(b) _(m) _(a)_(m) ^((m)) g_(b) ₁ _(. . . b) _(n) U_(a) _(m+1) _(b) _(m+1) ^((m+1)) .. . U_(a) _(n) _(b) _(n) ^((n)), where the first m unitary matrices arepushed to the left of the core tensor. The collection of unitarymatrices on either side of the core tensor can be decomposed into theirrotation angles and the gradient of each rotation angle is obtained bythe chain rule of calculus and the contraction of the gradients with theinput tensor and the error tensor.

The inventors have recognized and appreciated that the gradients of thephases (e.g., for decomposed matrices U and V) and the gradients of thesigned singular values (e.g., for matrix Σ) may have different upperbounds. Consider a task to compute the gradients of the scalar lossfunction L with respect to neural network parameters. In Euclideanspace, the value of the gradients is given by

$\frac{\partial L}{\partial W},$

where W is a matrix. In phase space, for a particular scalar phaseθ_(k), the chain rule provides:

$\frac{\partial L}{\partial W} = {\sum\limits_{ij}{\frac{\partial L}{\partial W_{ij}}\frac{\partial W_{ij}}{\partial\theta_{k}}}}$

From the definition of the trace, this is equal to:

$\frac{\partial L}{\partial\theta_{k}} = {{Tr}\left( {\frac{\partial L}{\partial W}\left( \frac{\partial W}{\partial\theta_{k}} \right)^{T}} \right)}$

where

$\frac{\partial L}{\partial\theta_{k}}\mspace{14mu}{and}\mspace{14mu}\frac{\partial W}{\partial\theta}$

both matrices. It is Known that the trace is bounded by the Frobeniusnorm product Tr(AB)≤∥A∥_(F)∥AB∥_(F) and that ∥A∥_(F)=∥A^(T)∥_(F).Therefore,

$\frac{\partial L}{\partial\theta_{k}} \leq {{\frac{\partial W}{\partial\theta_{k}}}_{F}{\frac{\partial L}{\partial W}}_{F}}$

Because differentiating with respect to θ does not change the singularvalues of W and thus does not change the Frobenius norm, the followingis true:

$\frac{\partial L}{\partial\theta_{k}} \leq {{W}_{F}{\frac{\partial L}{\partial W}}_{F}}$

Differentiating with respect to a particular singular value σ_(k), allof the singular values go to zero except for the one beingdifferentiated, which goes to 1, which means that

${\frac{\partial W}{\partial\theta_{k}}}_{F} = 1$

Therefore,

$\frac{\partial L}{\partial\theta_{k}} \leq {\frac{\partial L}{\partial W}}_{F}$

In some embodiments, the gradients of the phases and the singular valuesare scaled separately during updating the parameters of the angularrepresentation to, for example, account for the differences in upperbounds. By scaling the gradients separately, either the gradients of thephases or the gradients of the singular values may be rescaled to havethe same upper bound. In some embodiments, the gradients of the phasesare scaled by the Frobenius norm of the matrix. According to some updaterules, scaling the gradients of the phases and the singular valuesindependently equates to having different learning rates for thegradients of the phases and the gradients of the singular values.Accordingly, in some embodiments, a first learning rate for updating thesets of components for the U and V matrices is different than a secondlearning rate for updating the set of components for the Σ matrix.

The inventors have recognized and appreciated that once a weight matrixis decomposed into phase space, both the phases and the singular valuesmay not need to be updated in every iteration to obtain a good solution.Accordingly if only the singular values (but not the phases) are updatedsome fraction of the overall training time, during those epochs only O(n) parameters would need to be updated rather than O (n²), leading toimprovements in overall runtime. Updating only the singular values orthe phases during some iterations may be referred to as “parameterclamping.” In some embodiments, parameter clamping may be performedaccording to one or more of the following clamping techniques:

-   -   Fixed clamping: train all parameters for a certain number of        iterations, then only update the singular values subsequently    -   Cyclic clamping: Train all parameters for a number of epochs M,        then freeze the phases (i.e., only update the singular values)        for a number of epochs N. Resume training all parameters for        another M epochs, then freeze the phases for N epochs again.        Repeat until the total number of desired epochs has been        reached.    -   Warmup clamping: Train all parameters for some number of epochs        K, then begin cyclic clamping for the remaining number of        epochs.    -   Threshold clamping: Continue updating phases or singular values        until their updates are smaller than a threshold value ε

The inventors have recognized and appreciated that the architecture ofthe photonics processor may influence the complexity of thecalculations. For example, in the architecture shown in FIG. 1, thedetectors are only at one end of the photonic processor array, resultingin the column-by-column approach (e.g., using partial forward andbackward passes) for calculating the gradients, as described above. Inan alternate architecture, a detector may be arranged at every photonicelement (e.g., at each MZI) in the photonics array. For such anarchitecture, the column-by-column approach may be replaced with asingle forward pass and a single backward pass, where the output atevery column is read out simultaneously, therefore providing additionalcomputational acceleration. Intermediate solutions, where columns ofdetectors are placed intermittently throughout the array are alsocontemplated. Any addition of detector columns commensurately reducesthe number of partial forward and backward passes required for thegradient computation.

The techniques described above illustrate techniques for performing anupdate of weight matrix parameters while keeping all of the computationin phase space (e.g., using the angular representation of the matrix).In some embodiments, at least some of the calculations may be performedin a Euclidean vector space, whereas other calculations are performed inphase space. For example, the quantities needed to perform the updatemay be computed in phase space, as described above, but the actualupdating of the parameters may occur in a Euclidean vector space. Theupdated matrix calculated in the Euclidean vector space may then bere-decomposed into weight space for a next iteration. In Euclideanvector space, for a given layer, the update rule may be:

$\frac{\partial L}{\partial W_{ij}} = {x_{i}\delta_{j}}$

The δ in this computation can be calculated with a backward pass throughthe entire photonics processor in phase space. Then, the outer productabove between x and δ can be computed separately (e.g., off-chip). Oncethe updates are applied, the updated matrix can be re-decomposed and thedecomposed values can be used to set the phases for the photonicprocessor as described above.

III. Convolutional Layers

Convolution and cross-correlation are common signal processingoperations with many applications such as audio/video encoding,probability theory, image processing, and machine learning. The termsconvolution and cross-correlation generally refer to mathematicaloperations that accept, as input, two signals and produce, as output, athird signal which represents the similarity that exists between theinputs. The inventors have recognized and appreciated that computingconvolutions and cross-correlations may be computationallyresource-intensive. In particular, the inventors have developedtechniques for improving the computational speed and efficiency ofconvolutions and cross-correlations. Embodiments of these techniquesinclude computing convolutions and cross-correlations by transformingconvolution operations into a matrix-vector product and/or a product ofmulti-dimensional arrays. Embodiments of these techniques furtherinclude computing convolutions according to a discrete transform.

The inventors have further recognized and appreciated that computingconvolutions and cross-correlations may be performed in a variety ofways depending on the intended application. Input and output signals maybe discrete or continuous. The data values that the signals are composedof may be defined over a variety of numerical domains such as the realnumbers, the complex plane, or a finite integer ring. The signals mayhave any number of dimensions. The signals may also have multiplechannels, which is a technique commonly used in convolutional neuralnetworks (CNNs). The embodiments described herein may be implemented toaccommodate these variations in any combination.

Furthermore, embodiments of these techniques may be implemented in anysuitable computational system configured to perform matrix operations.Examples of such computational systems which may benefit from thetechniques described herein include central processing units (CPUs),graphic processing units (GPUs), field programmable gate arrays (FPGAs),application-specific integrated circuits (ASICs), and photonicprocessors. While embodiments described herein may be described inconnection to photonic processors, it is to be appreciated that thesetechniques may be applicable to other computational systems such as, butnot limited to, those described above.

Following below are more detailed descriptions of various conceptsrelated to, and embodiments of, techniques for computing convolutionsand cross-correlations. It should be appreciated that various aspectsdescribed herein may be implemented in any of numerous ways. Examples ofspecific implementations are provided herein for illustrative purposesonly. In addition, the various aspects described in the embodimentsbelow may be used alone or in any combination, and are not limited tothe combinations explicitly described herein.

The inventors further recognized and appreciated that using lightsignals, instead of electrical signals, overcomes many of theaforementioned problems with electrical computing. Light signals travelat the speed of light in the medium in which the light is traveling;thus the latency of photonic signals is far less of a limitation thanelectrical propagation delay. Additionally, no power is dissipated byincreasing the distance traveled by the light signals, opening up newtopologies and processor layouts that would not be feasible usingelectrical signals. Thus, light-based processors, such as a photonicprocessor, may have better speed and efficiency performance than someconventional electrical-based processors.

To implement a photonics-based processor, the inventors have recognizedand appreciated that the multiplication of an input vector by a matrixcan be accomplished by propagating coherent light signals, e.g., laserpulses, through a first array of interconnected variable beam splitters(VBSs), a second array of interconnected variable beam splitters, andmultiple controllable electro-optic elements between the two arrays thatconnect a single output of the first array to a single input of thesecond array.

Some embodiments of a photonic processor, as described herein, with noptical modes naturally compute a matrix-vector product between a matrixof size [n×n] and an n-element vector. This may be equivalentlyexpressed as a matrix-matrix product between matrices of sizes [n×n] and[n×1]. A sequence of K matrix-vector product operations with K differentinput vectors and a single, repeated input matrix may be expressed asthe computation of a matrix-matrix product between matrices of size[n×n] and [n×K]. But the applications and algorithms described hereinoften involve the computation of general matrix-matrix multiplication(GEMM) between matrices of arbitrary size; i.e., the computation:

c _(ik)=Σ_(j=1) ^(J) a _(ij) ·b _(jk) ∀i∈[1,I],k∈[1,K],

where a_(ij) is the element in the i^(th) row and j^(th) column of an[I×J] matrix A, b_(jk) is the j^(th) row and k^(th) column of a [J×K]matrix B and c_(ik) is the element in the i^(th) row and k^(th) columnof the [I×K] matrix C=AB. Due to the recursive nature of thiscomputation, this can be equivalently expressed as:

${C_{i} = {\sum\limits_{j = 1}^{\lbrack\frac{J}{n}\rbrack}\;{A_{ij}B_{j}\mspace{14mu}{\forall{i \in \left\lbrack {1,\left\lceil {I\text{/}n} \right\rceil} \right\rbrack}}}}},$

where A has been first padded to a [(n·┌I/n┐)×(n·┌J/n┐)] matrix and thenpartitioned into a [┌I/n┐×┌J/n┐] grid of submatrices (each of size[n×n]) and A_(ij) is the [n×n] submatrix in the i^(th) row and j^(th)column of this grid, B has been first padded to a [(n·┌J/n┐)×K] matrixand then partitioned into a [┌J/n┐×1] grid of submatrices (each of size[n×K]) and B_(j) is the [n×K] submatrix in the j^(th) row of this grid,and C has been first padded to a [(n·┌J/n┐)×K] matrix and thenpartitioned into a [┌I×n┐×1] grid of submatrices (each of size [n×K])and C_(i) is the [n×K] submatrix in the i^(th) row of this grid.

According to some embodiments, using this process, a photonic processorcan compute any GEMM by loading (┌I/n┐·┌J/n┐) different matrices intothe photonic array and, for each loaded matrix, propagating K differentvectors through the photonic array. This yields ┌I/n┐·┌J/n┐·K outputvectors (each comprised of n elements), a subset of which may be addedtogether to yield the desired [I×K] output matrix, as defined by theequation above.

The inventors have recognized and appreciated that a photonic processormay accelerate the process of computing convolutions andcross-correlations, but that embodiments for computing convolutions andcross-correlations described herein may be implemented on any suitablecomputational system. Embodiments described herein are discussed interms of 2-dimensional convolutions, but may be generalizable to anynumber of dimensions. For an [I_(h)×I_(w)] input (herein called the“image,” though it is to be understood that the input could representany suitable data), G, and a [K_(h)×K_(w)] filter, F, the mathematicalformula for a two-dimensional convolution is:

${\left( {G*F} \right)\left\lbrack {x,y} \right\rbrack} = {\sum\limits_{i = 0}^{K_{h}}\;{\sum\limits_{j = 0}^{K_{w}}\;{{F\left\lbrack {i,j} \right\rbrack} \cdot {\hat{G}\left\lbrack {{x - i},{y - j}} \right\rbrack}}}}$

The two-dimensional cross-correlation is given by:

${\left( {G\mspace{14mu}\bigstar\mspace{14mu} F} \right)\left\lbrack {x,y} \right\rbrack} = {\sum\limits_{i = 0}^{K_{h}}\;{\sum\limits_{j = 0}^{K_{w}}\;{\overset{\_}{F\left\lbrack {i,j} \right\rbrack} \cdot {\hat{G}\left\lbrack {{x + i},{y + j}} \right\rbrack}}}}$

where Ĝ is a function of G determined by the boundary conditions, Fdenotes complex-conjugation, and · denotes scalar multiplication.

In some implementations, convolution and cross-correlation operationsmay be interchangeable, as the cross-correlation of complex-valued,two-dimensional signals G and F can be converted to a convolution via

${\left( {G*F} \right)\left\lbrack {x,y} \right\rbrack} = {{\overset{\_}{\left( {\overset{\_}{G\left\lbrack {x,y} \right\rbrack}\mspace{14mu}\bigstar\mspace{14mu}{F\left\lbrack {{- x},{- y}} \right\rbrack}} \right)}\left\lbrack {x,y} \right\rbrack}.}$

The embodiments described herein will focus on the convolution case, butit is to be understood that embodiments described herein may be used tocompute convolutions and cross-correlations.

In both convolution and cross-correlation, different variants existdepending on how the boundary conditions are handled. Two boundaryconditions described in some embodiments herein include circular:

Ĝ[x,y]=G[x% I _(h) ,y % I _(w)]

and padded:

Ĝ[x,y]=G[x,y] if (0≤x≤I _(h) and 0≤y≤I _(w)); 0 otherwise

where a % n indicates a mod n.

Additional boundary condition variants may be used, according to someembodiments. These boundary conditions include symmetric (also known asmirror or reflective) boundary conditions in which the image isreflected across the boundary. The padded boundary condition mayvariously be called linear or fill in some embodiments. The circularboundary condition is also known as wrapped.

Additionally, different output modes may be employed to determine whichelements interact with the boundary condition. These output modesinclude valid, same (or half-padded), and full output modes. Validoutput mode requires that the output consists of only the elements thatdo not depend on the boundary condition. Same output mode requires theoutput to be the same size as the input. Full output mode requires thatthe output consists of all elements that do not exclusively depend onthe boundary condition.

Different output modes control the number of points [x, y] on which theoutput is defined. Each embodiment described herein may therefore bemodified to operate in any given output mode. While the embodimentsdescribed herein focus on the same-mode convolution case, it is to beunderstood that these implementations may be extended to computecross-correlation and/or alternate output modes instead or in additionto the embodiments described herein.

In some implementations, such as in CNNs, these operations may begeneralized such that they can be applied to and/or producemulti-channel data. As an example, an RGB image has three colorchannels. For an input, G, with two spatial dimensions and C channels,the multi-channel operation is defined as:

${{\left( {G \otimes F} \right)\left\lbrack {m,x,y} \right\rbrack} = {\sum\limits_{c = 0}^{C}\;{{F\left\lbrack {m,c} \right\rbrack} \otimes {G\lbrack c\rbrack}}}};{\forall{m \in M}}$

where {circle around (*)} represents either convolution orcross-correlation, M is the number of output channels, G is a threedimensional [C×I_(h)×I_(w)] tensor, F is a four-dimensional[M×C×I_(h)×I_(w)] tensor, and (G{circle around (*)}F) is athree-dimensional [M×I_(h)×I_(w)] tensor. For the above, slice indexingnotation is used, with spatial dimensions suppressed, such that F[m, c]accesses a two-dimensional [K_(h)×K_(w)] spatial slice of F and G[c]accesses a two-dimensional [I_(h)×I_(w)] spatial slice of G.

In general, techniques for expressing convolutions as matrix operationsmay follow the process of FIG. 3-1. In act 3-102, pre-processing of theimage and/or filter matrices may occur prior to the matrix operation inorder to make sure the matrices are, for example, of the correctdimensionality, obey boundary conditions, and/or output modes. In act3-104, the core matrix or matrix-vector operation may be applied,creating an output of the convolution. In act 3-106, post-processing ofthe output of the convolution may occur in order to, for example,reshape the output, as will be discussed in more detail herein.

Some embodiments may use a photonic processor to compute convolutions asa matrix-vector product. The inventors have recognized and appreciatedthat an array of variable beam splitters (VBSs), such as those that maybe included in some embodiments of a photonic processor as describedpreviously herein, may be used to represent any unitary matrix. As anexample, using those techniques to represent an expanded image G_(mat),the matrix may be decomposed with singular value decomposition as

G _(mat) =V ^(T) ΣU.

In some embodiments of a photonic processor, the two unitary matrices Uand V may then be decomposed with the algorithm described previously.The phases that result from that computation are programmed into thephotonic array, along with the singular values. In some embodiments, theprocessor decomposes the filter rather than the image so that the filtercan stay loaded for an entire batch of images.

An example of a process for computing a convolution in a photonicprocessor is shown in FIG. 3-2, according to some embodiments. In act3-202, the process constructs the matrix G_(mat) from the input imagematrix G. The matrix G_(mat) may be constructed in any suitable way inaccordance with the chosen boundary conditions, including but notlimited to constructing G_(mat) as a doubly-block circulant matrix or adoubly-block Toeplitz matrix.

In act 3-204, the decomposed matrix G_(mat) may then be loaded into thephotonic array. For each filter F in the input batch, a loop isrepeated, wherein the filter F is flattened into a column vector in act3-206, passed through the photonic array in act 3-208 to perform thematrix-multiplication operation, and then reshaped into an output withan appropriate dimensionality in act 3-210. In act 3-212, it isdetermined whether any further filters F remain. If further filters Fare to be passed through the convolutional layer, the process returns toact 3-206. Otherwise, the process ends. Because of the commutativenature of convolutions, process 3-200 may be performed with the filter Fexpanded into F_(mat) and the images G being flattened into columnvectors and passed through the photonic array in act 3-208.

A photonic processor may be used to implement any suitablematrix-multiplication-based algorithm. Matrix-multiplication-basedalgorithms re-order and/or expand the input signals such that thecomputation can be expressed as a general matrix-matrix multiply (GEMM)with some pre- and/or post-processing. Some examplematrix-multiplication-based algorithms which may be implemented on aphotonic processor include im2col, kn2row, and memory-efficientconvolution (MEC).

According to some embodiments, the im2col algorithm may be implementedon a photonic processor. In the im2col algorithm, during pre-processing,the image G may be expanded from an [I_(h)×I_(w)] matrix to a[(K_(h)·K_(w))×(I_(h)·I_(w))] matrix. The filter F may be flattened froma [K_(h)×K_(w)] matrix to a [1×(K_(h)·K_(w))] row vector. The output maythen be generated by a matrix-vector product of the image and the filterbecause this pre-processing step generates an expanded data matrix inwhich each column contains a copy of all (K_(h)×K_(w)) elements that maybe scaled and accumulated for each location in the output. The im2colalgorithm may therefore require O(K_(h)·K_(w)·I_(h)·I_(w)) data copiesand O(K_(h)·K_(w)·I_(h)·I_(w)) temporary storage.

According to some embodiments, the kn2row algorithm may be implementedon a photonic processor. The kn2row algorithm computes an outer productof the unmodified image and filter signals, generating a temporarymatrix of size [(K_(h)·K_(w))×(I_(h)·I_(w))]. The kn2row algorithm thenadds particular elements from each row of the outer product together toproduce a [1×(I_(h)·I_(w))] output vector. The kn2row algorithm maytherefore also require O(K_(h)·K_(w)·I_(h)·I_(w)) data copies andO(K_(h)·K_(w)·I_(h)·I_(w)) temporary storage.

According to some embodiments, the MEC algorithm may be implemented on aphotonic processor. The MEC algorithm may expand the input image by afactor of only K_(h) or K_(w), rather than a factor of (K_(h)·K_(w)) asin the im2col algorithm. If the smaller filter dimension is chosen forexpansion, then the algorithm requires only O(min(K_(h),K_(w))·I_(h)·I_(w)) temporary storage and data copies. Unlike im2col orkn2row, which compute a single matrix-vector product, the MEC algorithmcomputes a series of smaller matrix-vector products and concatenates theresults.

In the embodiments discussed above, the filter matrix may be expandedduring pre-processing rather than the image because of the commutativenature of convolutions. The choice of whether the image or the filter isto be tiled and reshaped into a matrix may be determined by whichoperations are faster and/or require less computational energy.

VII. Multi-Dimensional Convolution Via Two-Dimensional Matrix-MatrixMultiplication

The inventors have recognized and appreciated that thematrix-multiplication-based algorithms for computing convolutionsdiscussed above may not be suitable for some computing architectures orapplications. The inventors have further recognized and appreciated thatan approach that could combine the computational efficiency of im2col orkn2row with the memory-efficient features of the MEC algorithm would bebeneficial for the computation of convolutions and cross-correlations.In particular, the inventors have recognized that these benefits may beachieved by splitting the re-ordering and reshaping of input and outputmatrices between pre- and post-processing steps, and that such a methodmay be generalized to N-dimensional convolutions, where N≥2.

According to some embodiments, the Multi-Dimensional Convolution viaTwo-Dimensional Matrix-Matrix Multiplication algorithm (herein the“cng2” algorithm), includes three steps. At a high level, for anon-limiting example of a two-dimensional, circular convolution, apreprocessing step builds a [K_(w)×(I_(h)·I_(w))] matrix by replicatingand rotating the rows of the [I_(h)×I_(w)] input matrix, wherein in someimplementations “rotation” refers to a cyclic permutation of theelements of a vector, e.g. rotate ([1,2,3,4], −1)⇒[2,3,4,1]. In the GEMMstep, the product of the [K_(h)×K_(w)] filter matrix and the[K_(w)×(I_(h)·I_(w))] matrix from the pre-processing step is computed.In post-processing, the rows of the [K_(h)×(I_(h)·I_(w))] matrix createdby the GEMM are rotated and added to build the output.

According to some embodiments, the cng2 algorithm may be modified toimplement other boundary conditions. As an example, for the case ofpadded convolution during pre- and post-processing, the vector rows areshifted rather than rotated. That is, the elements that would otherwisewrap around the row vectors during the rotation step are set to zero.Other boundary conditions which may be implemented in the cng2 algorithminclude, but are not limited to, symmetric or mirror boundaryconditions.

Additionally, it may be noted that the preprocessing step of the cng2algorithm is not limited to being applied only to the left-hand-sideinput (the image, herein), but could rather be applied to theright-hand-side input (the filter, herein) according to someembodiments. For full- or valid-mode convolution, the operation iscommutative, and the pre-processing phase could be applied to eitherinput. For same-mode convolution, the operation is non-commutative whenI_(h)≠K_(h) or I_(w)·K_(w), but the pre-processing phase can still beapplied to the right-hand-side, though the filter must first bezero-padded and/or cropped in each dimension to match the output size.

In some implementations, the cng2 algorithm may include additionalsteps, as described in FIG. 3-3A. Prior to the pre-processing stage asdescribed previously, the input filter matrix or matrices may need to bereshaped into filter matrix F with appropriate dimensions, as shown inact 3-302. This reshaping may be done by concatenating the input filtermatrix or matrices in any suitable manner. In act 3-304, thepre-processing step of building circulant matrix H is performed, andwill be described in more detail with reference to FIG. 3-3B. In act3-306, the GEMM step, is performed and an intermediate matrix X=F×H iscreated. Next, post-processing steps may be performed. In act 3-308,vector rows of matrix X are rotated and/or shifted to form matrix X′. Inact 3-310, vector row addition is performed on the rows of matrix X′ toform matrix Z. Depending on the memory layout of the particularprocessing system, matrix Z may be reshaped into an at least one outputmatrix in act 3-312.

The method of building matrix H may depend on the desired boundaryconditions, as shown in an expansion of act 3-304 in FIG. 3-3B,according to some embodiments. In act 3-314, it may be determinedwhether the boundary condition is circular. If it is determined that theboundary condition is circular, the processing system may proceed to act3-316, wherein matrix H is created by replicating and rotating rows ofthe at least one input matrix. If, instead, in act 3-314, it isdetermined that the boundary condition is padded rather than circular,the processing system may proceed to act 3-318. In act 3-318, matrix His created by replicating and shifting the rows of the at least oneinput matrix, as discussed previously. It is to be understood that otherboundary conditions than a padded boundary condition may be employed inact 3-318 in some embodiments.

Alternately, according to some embodiments, when computing thecross-correlation the problem may not need to be explicitly convertedinto a convolution as in process 3-300. Instead, the element-reversalstep 3-302 may be omitted and the pre- and post-processing steps of thecng2 algorithm can be modified accordingly. That is, theelement-reversal step may be combined with the pre- and post-processingsteps of the cng2 algorithm. How this is done depends on whether thepre-processing expansion is applied to the left-hand-side orright-hand-side input. If the left-hand-side input is expanded, shiftsor rotations in both the pre- and post-processing steps may be carriedout in the opposite direction. If the right-hand-side input is expanded,each of the circulant matrices generated during the preprocessing phasemay be transposed and concatenated in the reverse order and the i^(th)row of the GEMM output matrix may be shifted or rotated by (i−n+1)·nelements rather than i·n elements in the post-processing phase. Forcomplex-valued data the cross-correlation still requires complexconjugation of one input.

In some implementations, such as in CNNs, it may be desirable togeneralize the above-described operations so that they can be applied toand/or produce multi-channel data. For a problem with C input channelsand M output channels, the filter matrix takes the form[(M·K_(h))×(K_(w)·C)], the input matrix takes the form[(K_(w)·C)×(I_(h)·I_(w))], and the output matrix takes the form[(M·K_(h))×(I_(h)·I_(w))].

Referring to FIGS. 3-4A through 3-4F, an example of process 3-300 formulti-channel inputs and outputs is depicted, according to someembodiments. For [2×2] filters, f, comprising 4 output channels and[3×3] images, G, comprising 3 input channels, act 3-402 is visualized inFIG. 3-4A. However, any size filter matrices, image matrices, and/ornumber of input and/or output channels may be implemented. In theexample of act 3-402, reshaping of the filters f into the [6×8] filtermatrix F is performed. In this example, reshaping of filters f is doneby concatenating filters f without otherwise altering the ordering ofthe matrix elements, though other methods such as rotating, shifting, orotherwise altering rows of filters f may be employed. The reshaping offilters f ensures that filter matrix F is of the appropriatedimensionality for the later GEMM operation. However, in someimplementations, wherein the memory is laid out appropriately, act 3-402may not be required prior to act 3-404 as described below.

According to some embodiments, after act 3-402, pre-processing of imageG may be performed in act 3-404, as depicted in FIG. 3-4B. In thisexample, image G is formed to perform same-mode convolution, though anyoutput mode may be used. Circulant matrix H in this example is formedbased on circular boundary conditions, though any boundary conditionssuch as padded boundary conditions, for example, may be used. After act3-404, the GEMM operation F×H=X of act 3-406 may be performed, asdepicted in FIG. 3-4C. In this example, intermediate matrix X hasdimensions of [9×8].

After the GEMM operation, post-processing steps may occur, as depictedin FIG. 3-4D through 3-4E. In FIG. 3-4D, act 3-408 is depicted, whereinrows of the intermediate matrix X are rotated to form matrix X′ inaccordance with the circular boundary conditions of this example. Otherboundary conditions, such as padded boundary conditions, as anon-limiting example, may be implemented in pre- and post-processing, aslong as the boundary conditions of the pre- and post-processing stepsare identical to one another.

Referring to FIG. 3-4E, in which act 3-410 is depicted, the next step ofpost-processing adds rows of the matrix X′ to form output matrix Z. Thatis, in this example, x₀₀+x₁₆=z₀₀, x₀₁+x₁₇=z₀₁, etc. In someimplementations, depending on how the memory of the processing system islaid out, reshaping of output matrix Z may have to be performed in act3-412 after act 3-410. In the example visualization of FIG. 3-4F, outputmatrix Z is reshaped into four output matrices A of dimensions [3×3].

In addition to being generalizable to multiple input channels, the cng2algorithm may be generalized to higher-dimensional signals (i.e. greaterthan two), according to some embodiments. For an n-dimensionalconvolution between a filter tensor of size [K_(n)×K_(n-1)× . . . ×K₁]and an image of size [I_(n)×I_(n-1)× . . . ×I₁], it is possible tocompute the desired output using two-dimensional matrix multiplicationwith similar steps to those taken for two-dimensional signals. Duringpre-processing, the input tensor may be expanded by a factor of(K_(a)·K_(a−1)· . . . ·K₁), where a may be thought of as the number ofdimensions handled during the pre-processing phase and any value in therange 1≤a≤n−1 may be chosen. In the GEMM step, a product of the filtertensor partitioned as a [(K_(n)·K_(n-1)· . . . ·K_(a+1))×(K_(a)·K_(a−1)·. . . ·K₁)] matrix and the expanded matrix from the pre-processing stepmay be performed. During the post-processing step, the subvectors of thematrix produced during the GEMM may be rotated and accumulated.

The expanded matrix produced by the pre-processing phase may consist of(I_(i)·I_(n-1)· . . . ·I_(a+i)) horizontally-concatentated submatriceswhere each submatrix is a nested Toeplitz matrix of degree a and theinnermost Toeplitz matrices are defined as they are in a two-dimensionalcng2 implementation. The post-processing phase may perform (n−a) roundsof rotations and additions where the i^(th) round partitions the matrixproduced by the previous round (or, initially, by the GEMM operation)into submatrices of size [K_(a+i)×(I_(a+i)·I_(a+i−1)· . . . ·I₁)]. Foreach submatrix, the following operations are then performed. First, thej^(th) row may be rotated or shifted by (j·(I_(a+i−1)·I_(a+i−2)· . . .·I₁)) elements. Then, all rows may be added together.

While the above description handles the dimensions in order, that is thepre-processing phase expands the data along the first a dimensions andthe post-processing phase reduces the data along the final n−adimensions, according to some embodiments, this does not need to be thecase. The pre-processing phase could expand the data along any adimensions by re-ordering the input and output data in the same manneras was described for the two-dimensional case.

The cng2 algorithm offers a flexible framework for computingconvolutions, with several alternate embodiments described herein. Insome implementations, the overlapping regions of the input signals for agiven point in the output may be shifted by a constant offset. Such anoffset may be applied regardless of output mode but is most often pairedwith same-mode output. For convolution (cross-correlation) operating insame-mode and the definitions given above, the boundary condition may beapplied to (K_(h)−1)·I_(w) elements along the top (bottom) edge and(K_(w)−1)·I_(h) elements along the left (right) edge of the input imageG. This behavior may be altered by redefining the operation with aconstant offset between the filter and output locations. When computingthe convolution (cross-correlation), this modification can be applied tocng2 by subtracting (adding) the offset to the shift or rotation amountsin the pre-processing phase and by subtracting (adding) offset·I_(w) tothe shift or rotation amounts in the post-processing phase.

Additionally, methods that have been proposed for reducing both the timeand storage requirements of the kn2row post-processing step maysimilarly be applied to the cng2 algorithm, according to someembodiments. For the kn2row algorithm, the GEMM operation may be brokeninto a series of K_(w)·K_(h) smaller GEMM operations, wherein theresults of those smaller GEMM operations are continually accumulatedtogether. This enables post-processing additions to be performed both inan optimized fashion and in-place with respect to the final output'sstorage. In the case of kn2row, this only works if the boundaryconditions can be ignored or if an additional (and generallyinefficient) so-called hole-punching process is introduced. But, in thecase of the cng2 algorithm, this process can be applied directly withoutsacrificing accuracy or additional processing, effectively eliminatingthe computational cost of the post processing step and reducing therequired temporary storage for the cng2 algorithm toO(K_(w)·I_(h)·I_(w)).

In some embodiments, the spatial dimensions could be handled in theopposite order as described in process 3-300. The cng2 algorithm couldbe augmented with transpose operations applied to both input signals atthe start of process 3-300 as well as a transpose operation on the finaloutput. This still produces the desired result but changes the behaviorwhen the shape of the filter is strictly rectangular (i.e. K_(w)≠K_(h)).In this case, the input image is expanded by a factor of K_(h) ratherthan K_(w) and the post-processing step consists of O(K_(w)·I_(h)·I_(w))additions rather than O(K_(h)·I_(h)·I_(w)). An implementation thatcombines this variant with the low-memory integrated-post-processingvariant above can further reduce the required temporary storage for thecng2 algorithm to O(min(K_(h), K_(w))·I_(h)·I_(w)).

As an alternative implementation, the rows and/or columns in thematrices that are passed to the GEMM operation may be re-ordered. If theGEMM operation is defined as C=AB, the rows and/or columns of eitherinput matrix A or B, may be re-ordered so long as the appropriatepermutation is applied to the other input matrix (in the case ofre-ordering the columns of A or rows of B) or the output matrix (in thecase of re-ordering the rows of A or the columns of B). In particular,re-ordering the rows of A in the case of multiple output channels mayreorganize the data-level parallelism available in the post-processingphase in a manner that is well suited for vector processors orsingle-instruction-multiple-data (SIMD) architectures.

The convolution computation may also be computed with a stride,according to some embodiments. For stride S_(x) in the first dimensionand stride S_(y) in the second dimension, the convolution operation isdefined as follows:

${\left( {G*F} \right)\left\lbrack {x,y} \right\rbrack} = {\sum\limits_{i = 0}^{K_{h}}\;{\sum\limits_{j = 0}^{K_{w}}\;{{F\left\lbrack {i,j} \right\rbrack} \cdot {\hat{G}\left\lbrack {{{x \cdot S_{x}} - i},{{y \cdot S_{y}} - j}} \right\rbrack}}}}$

This definition reduces the size of the output signal by a factor ofS_(x)·S_(y), and is equivalent to increasing the step size by which thefilter is slid across the image for each output point. This may beimplemented by computing the un-strided convolution and thendown-sampling the result by the appropriate amount in each dimension,but this requires O(S_(x)·S_(y)) more computation steps than necessary.At a minimum, this computational penalty can be reduced in cng2 toO(S_(y)) by modifying the pre-processing phase 3-304 to generate onlyevery S_(x) ^(th) column in each individual circulant matrix andmodifying the post-processing phase to shift or rotate each row byi·(I_(w)/S_(x)) rather than i·I_(w). In some implementations, thecomputational penalty can be completely eliminated with additionalmodifications to each phase. First, the preprocessing step 3-304 may bemodified to produce S_(y) expanded matrices rather than a single matrix,where the i^(th) circulant matrix is assigned to the j^(th) expandedmatrix if j=i mod S. The core processing phase must then perform S_(y)GEMM operations—one GEMM operation per expanded input matrix—each ofwhich uses only K_(w)/S_(y) rows of the filter matrix. Thepost-processing steps 3-308, 3-310 may then interleave the rows of theresulting matrices, add each group of S_(y) rows directly (i.e., withoutshifting or rotating the rows), and run the K_(w)/S_(y) rows through thestandard post-processing logic with shift or rotation amounts ofi·(I_(w)/S_(x)).

Alternately, the convolution may be dilated, according to someembodiments. For dilation D_(x) in the first dimension and dilationD_(y) in the second dimension, the convolution operation is defined as:

${\left( {G*F} \right)\left\lbrack {x,y} \right\rbrack} = {\sum\limits_{i = 0}^{K_{h}}\;{\sum\limits_{j = 0}^{K_{w}}\;{{F\left\lbrack {i,j} \right\rbrack} \cdot {\hat{G}\left\lbrack {{x - {i \cdot D_{x}}},{y - {j \cdot D_{y}}}} \right\rbrack}}}}$

Dilation increases the receptive field of the filter across a largerpatch of the image for each output point, and may be viewed as insertingspaces between elements of the filter. The cng2 algorithm may bemodified to implement dilated convolution by increasing the rotation orshift amounts in both the pre- and post-processing phases by D_(x) andD_(y), respectively. A dilated convolution may be further restricted tobeing computed with a causal output mode.

The inventors have further recognized and appreciated that convolutionsand cross-correlations may be computed by using a transform-basedalgorithm. Transform-based algorithms change the nature of thecomputational problem by first computing the equivalent representationof the input signals in an alternative numerical domain (e.g. frequencydomain), performing an alternative linear operation (e.g. element-wisemultiplication), and then computing the inverse transform of the resultto return to the signal's original numerical domain (e.g. time domain).Examples of such transforms include discrete Fourier transforms (DFT),discrete sine transforms, discrete cosine transforms, discrete Hartleytransforms, undecimated discrete wavelet transforms, Walsh-Hadamardtransforms, Hankel transforms, and finite impulse response (FIR) filterssuch as Winograd's minimal filtering algorithm. An example of atransform-based algorithm based on a DFT will be described herein, butany suitable transform may be implemented in a transform-based algorithmand on a photonic processor.

For unitary normalization, the discrete Fourier transform (DFT) of aone-dimensional signal is computed as

${X(k)} = {{\mathcal{T}_{1D}\left( {x(n)} \right)} = {\frac{1}{\sqrt{N}}{\sum\limits_{n = 0}^{N - 1}\;{{x(n)} \cdot {e^{{- \frac{2\pi\; i}{N}}{kn}}.}}}}}$

The inverse of this transform

⁻¹ may be computed by taking the complex conjugate. Similarly, in twodimensions, the unitary normalized DFT may be computed as

${X\left( {k,l} \right)} = {{\mathcal{T}_{2D}\left( {x\left( {m,n} \right)} \right)} = {\frac{1}{\sqrt{N}}\frac{1}{\sqrt{M}}{\sum\limits_{m = 0}^{M}\;{\sum\limits_{n = 0}^{N}\;{{x\left( {m,n} \right)} \cdot {e^{{- 2}\pi\;{i{({\frac{km}{M} + \frac{\ln}{N}})}}}.}}}}}}$

Performing the one-dimensional DFT defined above on a vector of size Ncan be accomplished by computing a matrix-vector product

(x)=X=Wx. The matrix W is referred to as the transform matrix, given by

$W = {\frac{1}{\sqrt{N}}\begin{pmatrix}1 & 1 & 1 & 1 & \cdots & 1 \\1 & \omega & \omega^{2} & \omega^{3} & \cdots & \omega^{N - 1} \\1 & \omega^{2} & \omega^{4} & \omega^{6} & \cdots & \omega^{2{({N - 1})}} \\1 & \omega^{3} & \omega^{6} & \omega^{9} & \cdots & \omega^{3{({N - 1})}} \\\vdots & \vdots & \vdots & \vdots & \ddots & \vdots \\1 & \omega^{N - 1} & \omega^{2{({N - 1})}} & \omega^{3{({N - 1})}} & \cdots & \omega^{{({N - 1})}{({N - 1})}}\end{pmatrix}}$

where

$\omega \equiv {\frac{1}{\sqrt{N}}{e^{- \frac{2\pi\; i}{N}}.}}$

The inverse transform may be computed by a similar matrix-vector productwhere the elements of W⁻¹ are the complex conjugates. The DFT is aseparable transform, so it may be regarded as computing twoone-dimensional transforms along orthogonal axes. Thus, thetwo-dimensional DFT of an [M×N] (i.e. rectangular) input x may becomputed via the following matrix triple product:

(x)=X=W×Y ^(T),

where W is an [M×M] transform matrix associated with the columns and Yis an [N×N] transform matrix associated with the rows, and thesuperscript T indicates the matrix transpose. In the case of a squareinput x of size [N×N], the transform matrix for the columns W is thesame as the transform matrix for the rows Y.

Equivalently, this may be computed by first flattening x row-wise into acolumn vector x_(col) of size M·N and computing the followingmatrix-vector product:

X _(col)=(W⊗Y)x _(col),

where ⊗ is the Kronecker product. According to some embodiments, theresult vector X_(col) may then be reshaped into an [M×N] two-dimensionalarray X:

(x)=X=reshape(X _(col)).

A similar process may be performed for other discrete transforms whereforward transform matrix W and the matrix W⁻¹ associated with theinverse transform are defined in any suitable way in accordance withsaid other transforms.

In the case of one-dimensional DFT, the matrix W is a unitary matrix,and may therefore be programmed directly into the photonic array inaccordance with previously described embodiments. For other discretetransforms, the matrix W may not be unitary and thus requiredecomposition before being programmed into the photonic array inaccordance with previously described methods. A process 3-500, accordingto some embodiments, for performing a one-dimensional transform on avector is shown in FIG. 3-5. In act 3-502, the matrix W is programmedinto the photonic array, and in act 3-504, the vector x is propagatedthrough the array.

In some implementations, a two-dimensional transform may be computed asdescribed in process 3-600 of FIG. 3-6. For a two-dimensional input xthat is of size N-by-N, process 3-500 may be modified to produce atwo-dimensional transform as defined above. In act 3-602, a transformmatrix W_(small) is created corresponding to a size N one-dimensionalinput. Next, in act 3-604, a block-diagonal matrix B of size N²-by-N² iscreated by tiling W along the diagonal. A column vector x_(col) of sizeN² is then created by flattening the input x row-wise in act 3-606. Inact 3-608, by propagating the vector x through the photonic array, themultiplication X_(partial)=Bx may be performed. In act 3-610, the matrixX_(partial) may then be reshaped into an N-by-N matrix which can befurther flattened column-wise into a size N² column vector in act 3-612.In act 3-614, the multiplication X=BX_(partial) may be performed bypropagating X_(partial) through the photonic array. In act 3-616, theresulting vector X may be reshaped into an N-by-N matrix for output.

In some embodiments, the two-dimensional transform of an [N×N] input xmay then be computed by first programming the matrix W into the photonicarray. Second, computing X_(partial)=Wx by propagating the columns of xthrough the photonic array. Third, transposing the partial resultX_(partial). Fourth, propagating the columns of X_(partial) ^(T) throughthe array a second time to compute WX_(partial) ^(T). Finallytransposing the result to produce X=W×W^(T).

Some systems, such as one embodiment of the photonic-based systemsdescribed herein, are limited to implementing real unitary matrices(that is, orthogonal matrices). In such implementations, the transformcan still be computed, but additional steps are needed. The system mustkeep track of the real and imaginary parts of the transform matrix andinput vector or image separately. The embodiments defined above forcomputing the products can be adapted for orthogonal matrices, exceptthat for every pass through the photonic array as described above, thealgorithm must perform four passes. Denoting the real part of a variableas Re(x) and imaginary part as Im(x), the real part of the product isRe(Wx)=Re(W)Re(x)−Im(W)Im(x) and similarly the imaginary part of theproduct is Im(Wx)=Re(W)Im(x)+Im(W)Re(x). According to some embodiments,in the photocore of a photonic processor representing only realmatrices, the process 3-700 of FIG. 3-7 may be carried out. According tothe dimensionality of the input, either process 3-500 or 3-600 may beused in process 3-700. In act 3-702, Re(W) is loaded into the photonicarray. In act 3-704, either process 3-500 or 3-600 may be performed,depending on the dimensionality of the input, for Re(x) and im(x). Thisproduces Re(W)Re(x) and Re(W)Im(x). In act 3-706, im(W) is loaded intothe photonic array. In act 3-708, either process 3-500 or 3-600 may beperformed, depending again on the dimensionality of the input, for Re(x)and Im(x). This produces Im(W)Re(x) and Im(W)Im(x). In act 3-710,Re(W)Re(x) and Im(W)Im(x) are subtracted to produce Re(Wx). And, in act3-712, Re(W)Im(x) and Im(W)Re(x) are added to produce Im(Wx).

With the above-described processes 3-500, 3-600, and 3-700, an inputmatrix may be converted into its transform. Once the convolutionalfilter F and image G are converted to their transform counterparts, theconvolution theorem may be applied, according to some embodiments. Theconvolution theorem states that a convolution of two signals correspondsto the transform of an element-wise product of the two signals'transforms. Mathematically, this is represented by:

(G*F)=T(G)⊙

(F),

or, equivalently,

G*F=

⁻¹(

(G)⊙

(F)),

where ⊙ represents element-wise multiplication, and

⁻¹ represents the inverse transform. In some embodiments, the dimensionsof the image and of the filter may differ; in such a case it is to beappreciated that the appropriate dimension transform matrices may beused to compute each of the forward transforms and the inversetransform. The matrix-multiplication equation representing theone-dimensional convolution with a general transform and generaldimensionality of the filter and image is thus:

G*F=W _(A) ^(T)((W _(B) F)⊙(W _(D) ^(T) G)),

where W_(B) is the matrix associated with the transform of the filter F,W_(D) ^(T) is the matrix associated with the transform of the image G,and W_(A) ^(T) is the matrix associated with the inverse transform ofthe combined signal.

Similarly, the matrix-multiplication equation representing thetwo-dimensional convolution with a general transform on rectangularfilters and images is thus:

G*F=W _(A) ^(T)((W _(B) FW _(C) ^(T))⊙(W _(D) ^(T) GW _(E)))W _(F),

where W_(B) and W_(C) ^(T) are the matrices associated with thetransform of the filter F, W_(D) ^(T) and W_(E) are the matricesassociated with the transform of the image G, and W_(A) ^(T) and W_(F)are the matrices associated with the inverse transform of the combinedsignal.

Referring to FIG. 3-8, some embodiments of performing convolutions inthe photocore of a photonic processor using a transform-based algorithmmay include the following acts. In act 3-802, a transform of image G maybe performed using any one of processes 3-500, 3-600, and/or 3-700.

In some embodiments, the filter F may then be padded with zeros in act3-804 to match the size of the image G, after which a transform may beperformed on filter F using any one of processes 3-500, 3-600, and/or3-700 in act 3-806. In act 3-808, the transformed filter F may then beloaded into the element-wise multiplier of the photonic array, and inact 3-810, an image G may be propagated through the photonic array. Inact 3-812, an inverse transform may be performed on the result of theprevious computation using any one of processes 3-500, 3-600, and/or3-700. The result of act 3-812 may then be reshaped in act 3-814 intothe size of G and cropped to produce the final convolved image, G*F.

In some embodiments, the convolution G*F may be computed in adivide-and-conquer fashion where one input is partitioned into a set oftiles and each tile is convolved with the second input separately. Theresults of each individual convolution can then be recombined into thedesired output, but the algorithms (e.g., overlap-add, overlap-save) forimplementing this divide-and-conquer approach are non-trivial. When oneinput is much smaller than the other and a transform-based algorithm isused for the convolution operation, this approach can be much moreefficient than computing the entire convolution in one operation asdescribed above with the filter being padded to match the size of theimage. It may be appreciated that by performing the transformations ofthe tiles on a photonic array, such a divide-and-conquer algorithm fortransform-based convolutions may be implemented on a photonic processor.

In some embodiments, the filter F and the image G may have multiplechannels. As defined above, this means each channel of the image isconvolved with the corresponding channel of the filter tensor, and theresults are added together element-wise. When a multi-channelconvolution is computed with a transform-based method, the summationacross channels may be performed in either the transform or the outputdomain. In practice, it is often chosen to perform the summation in thetransform domain because this decreases the amount of data on which theoutput transform must be applied. In this case, the element-wisemultiplication followed by channel-wise summation can be expressed as asequence of matrix-matrix multiplications (GEMMs). Mathematically, thiscan be expressed as follows:

Let G be an input signal comprising C data channels of N×N images. Let Fbe an input signal comprising M·C data channels of N×N filters. Let Cand M be the number of input and output data channels, respectively. LetQ_(m,c) be transformed data of the m^(th) output channel and the c^(th)input channel of the filter tensor (i.e., Q_(m,c)=W_(B)F_(m,c)W_(C)^(T)). Let R be the transformed three-dimensional [C×N×N] input tensorand R_(c) be the c^(th) channel of the transformed input tensor (i.e.,R_(c)=W_(D) ^(T) G_(c)W_(E)). Then, the convolution of F and G producingmultiple output channels is:

(G*F)_(m) =W _(A) ^(T)(Σ_(c=1) ^(c) Q _(m,c) ⊙R _(C))W _(F) ∀m∈[1,M].

If S^(ij) denotes a column vector comprised of the C elements in the (i,j)^(th) position of each channel in a three-dimensional [C×N×N] tensorS, this can be equivalently expressed as:

(G*F)_(m) ^(ij) =W _(A) ^(T)(Q _(m) ^(ij) R ^(ij))W _(F)∀i∈[1,N],j∈[1,N],m∈[1,M]

Each of the Q_(m) ^(ij)R^(ij) matrix-matrix multiplications may becomputed on a photonic processor as described above. This may further becombined with the divide-and-conquer approaches described above.

Aspects of the present application provide methods, procedures andalgorithms which may be performed on a processing device, such as a CPU,GPU, ASIC, FPGA or any other suitable processor. For example, theprocessing device may perform the procedures described above to generatesettings for the variable beam splitters and modulators of the photocoreof the photonic processor described herein. The processing device mayalso perform the procedures described above to generate the input datato be input into the photonic processor described herein.

One example implementation of a computing device may include at leastone processor and a non-transitory computer-readable storage medium. Thecomputing device may be, for example, a desktop or laptop personalcomputer, a personal digital assistant (PDA), a smart mobile phone, atablet computer, a server, or any other suitable computing device. Thecomputer-readable media may be adapted to store data to be processedand/or instructions to be executed by processor. The processor enablesprocessing of data and execution of instructions. The data andinstructions may be stored on the computer-readable storage media andmay, for example, enable communication between components of thecomputing device. The data and instructions stored on computer-readablestorage media may comprise computer-executable instructions implementingtechniques which operate according to the principles described herein.

A computing device may additionally have one or more components andperipherals, including input and output devices. These devices can beused, among other things, to present a user interface. Examples ofoutput devices that can be used to provide a user interface includeprinters or display screens for visual presentation of output andspeakers or other sound generating devices for audible presentation ofoutput. Examples of input devices that can be used for a user interfaceinclude keyboards, and pointing devices, such as mice, touch pads, anddigitizing tablets. As another example, a computing device may receiveinput information through speech recognition or in other audible format.As another example, a computing device may receive input from a camera,lidar, or other device that produces visual data.

Embodiments of a computing device may also include a photonic processor,such as the one described herein. The processor of the computing devicemay send and receive information to the photonic processor via one ormore interfaces. The information that is sent and received may includesettings of the variable beam splitters and modulators of the photonicprocessor and/or measurement results from the detectors of the photonicprocessor.

IV. Photonic Encoder

The inventors have recognized and appreciated that phase-intensityrelationships, whereby phase and intensity modulation of optical signalsare interdependent of one another, pose a challenge for preciselyencoding vectors in light fields for optical processing.

Some optical modulators encode numeric vectors in the optical domain,where numbers are encoded onto the phase or the intensity of an opticalsignal. In an ideal world, an optical intensity modulator is capable ofmodulating the intensity of an optical signal while maintaining thephase of the optical signal unchanged, and an optical phase modulator iscapable of modulating the phase of an optical signal while maintainingthe intensity of the optical signal unchanged. In the real world,however, the phase and the intensity of an optical signal are mutuallyinterdependent. As such, intensity modulation gives rise to acorresponding phase modulation and phase modulation gives rise to acorresponding intensity modulation.

Consider for example integrated photonics platforms, in which intensityand phase are related to one another by the Kramers-Kronig equations(bidirectional mathematical relations connecting the real and imaginaryparts of a complex analytic function). In these cases, the optical fieldcan be represented by a complex function where the real part and theimaginary part of the function (and as a result, the intensity and thephase) are related to one another. In addition, realisticimplementations of intensity and phase modulators generally suffer fromdynamic loss whereby the amount of modulation they impart (phase orintensity) depends on their current settings. These modulatorsexperience a certain power loss when no phase modulation occurs, andexperience a different power loss when phase modulation occurs. Forexample, the power loss experienced at no phase modulation may be L1,the power loss experienced at a π/2-phase modulation may be L2, and thepower loss experienced at a n-phase modulation may be L3, with L1, L2and L3 being different from each other. This behavior is undesirablebecause, in addition to phase modulation, the signal further experiencesintensity modulation.

An example of an optical modulator (4-10) is depicted in FIG. 4-1, whereψ_(in) represents an input optical field, ψ_(out) represents the opticalfield output by modulator 4-10, α represents the amplitude modulationfactor and θ represents the phase modulation factor. Ideally, modulator4-10 would be able to set the entries of the optical field ω_(in) to anyphase in combination with any attenuation as ω_(out)=αe^(iθ), with α≤1and θ∈[0,2π]. Because modulators typically have an associatedKramers-Kronig relation, and/or are subject to dynamic loss, simplysetting the values of α and θ cannot be easily accomplished.Conventionally, two or more modulators with two independentlycontrollable modulating signals are used to encode both the amplitudeand phase of the optical field in a way that is representative of areal, signed number. However, having multiple controllable modulatingsignals calls for complicated multi-variable encoding schemes, ofteninvolving feedback loops for simultaneously controlling the phase andintensity of the optical signal.

Optical modulators that are capable of modulating intensity withoutaffecting the phase exist, including modulators based on electro-opticmaterials. Unfortunately, electro-optic modulators are challenging towork with in large-scale commercial contexts due the fact that theyinvolve use of materials impractical to fabricate.

Conventional computers do not natively perform complex arithmetic.Rather, they use signed arithmetic where numbers can be positive ornegative and complex arithmetic can be built up by performing severalcalculations and combining the results. The inventors have recognizedand appreciated that this fact may be exploited to dramatically simplifythe implementation of real, signed linear transformations, even in thepresence of dynamic loss and/or non-ideal phase or intensity modulators.

Some embodiments relate to techniques for encoding vectors of signed,real numbers using non-ideal optical modulators (modulators in whichintensity modulation gives rise to phase modulation and phase modulationgives rise to intensity modulation). Contrary to other implementations,some such embodiments involve use of a single modulating signal tocontrol the phase as well as the intensity of an optical signal. Theinventors have appreciated that, when encoding signed, real numbers ontooptical field using a single modulating signal, the exact location ofthe optical signal in phase space (e.g., the real and imaginary part, orthe amplitude and phase) is not important for purposes of decoding. Whatmatters for accurate decoding, according to some embodiments, is theprojection of the optical signal on the real axis (or other arbitraryaxes pre-selected as the measurement axis). To perform the projection onthe pre-selected axis, in some embodiments, coherent detection schemesare used.

Optical domain encoding techniques of the types described herein may beused in a variety of contexts, including but not limited to high-speedtelecommunications for short, mid and long-haul applications, onchip-phase sensitive measurements for sensing, communications andcomputing, and optical machine learning using photonic processors.

More generally, encoding techniques of the types described herein may beused in any context in which optical signals are processed according toreal transformations (as opposed to complex transformations).Conventional computers do not directly perform complex arithmetic.Rather, conventional computers use signed, real arithmetic where numberscan be positive or negative. In some embodiments, complex arithmetic canbe built up in the optical domain by performing several calculationsinvolving real number arithmetic. Consider for example an optical linearsystem configured to transform optical signals according to thefollowing expression:

y = Mx = [Re(M) + iIm(M)][Re(x) + iIm(x)] =   [Re(M)Re(x) − Im(M)Im(x)] + i[Re(m)Im(x) + Im(M)(Re(x)]

where x represents an input vector, M represents a transformationmatrix, y represents the output vector and i represents the imaginarynumber defined such that i²=−1. Considering real transformations (suchthat Im(M)=0), y can be rewritten as follows:

$y = {{e^{i\;\gamma}{M\left\lbrack {{{Re}(x)} + {{iIm}(x)}} \right\rbrack}} = {{e^{i\;\gamma}{{MRe}(x)}} + {e^{i{({\gamma + \frac{\pi}{2}})}}{{MIm}(x)}}}}$

It should be noted that, based on this equation, the real and imaginaryparts of the input field x contribute only to linear independentcomponents of the resulting field y. The inventors have recognized andappreciated that y can be decoded using a coherent receiver byprojecting y on any arbitrary axis in the complex plane.

In some embodiments, encoding techniques of the types described hereinmay be used as part of a photonic processing system. For example, insome embodiments, optical encoder 1-101 of FIG. 1-1 and/or opticalencoders 1-1103 of FIG. 1-11 and/or optical encoders 1-1211 of FIG. 1-12B may implement encoding techniques of the types described herein.

FIG. 4-2A is a block diagram of a photonic system implementing opticalencoding techniques according to some embodiments. Photonic system 4-100includes an optical source 4-102, an encoder 4-104, an optical modulator4-106, and optical transformation unit 4-108, a coherent receiver 4-110,a local oscillator 4-112 and a decoder 4-114. In some embodiments,photonic system 4-100 may include additional or alternative componentsnot illustrated in FIG. 4-2A. In some embodiments, some or all of thecomponents of FIG. 4-2A may be disposed on the same semiconductorsubstrate (e.g., a silicon substrate).

Optical source 4-102 may be implemented in a number of ways, including,for example, using optical coherent sources. In one example, opticalsource 4-102 includes a laser configured to emit light at wavelength λ₀.The wavelength of emission may be in the visible, infrared (includingnear infrared, mid infrared and far infrared) or ultraviolet portion ofthe electromagnetic spectrum. In some embodiments, λ₀ may be in theO-band, C-band or L-band. Light emitted by optical source 4-102 ismodulated using optical modulator 4-106. Optical modulator 4-106 is anon-ideal modulator, such that a phase modulation gives rise to anintensity modulation and an intensity modulation gives rise to a phasemodulation. In some embodiments, the phase modulation may be related tothe intensity modulation according to the Kramers-Kronig equations.Alternatively, or additionally, modulator 4-106 may suffer from dynamicloss where a phase shift leads to attenuation.

In some embodiments, modulator 4-106 is driven by a single electricalmodulating signal 4-105. Thus, a single modulating signal modulates boththe phase and the amplitude of the optical field. This is in contrast tomodulators conventionally used in optical communications to encodesymbols in the complex amplitude of an optical field, where each symbolrepresents more than one bit. In such types of modulators, in fact,multiple modulating signals modulate the optical field. Consider forexample optical modulators configured to provide quadrature phase-shiftkeying (QPSK) modulation schemes. In these types of modulators, onemodulating signal modulates the real part of the optical field and onemodulating field modulates the imaginary part of the optical field.Thus, in QPSK modulators, the phase and the amplitude of the opticalfield are modulated, collectively, using two modulating signals.

Examples of modulators that may be used for modulator 4-106 include MachZehnder modulators, electro-optical modulators, ring or disk modulatorsor other types of resonant modulators, electro-absorption modulators,Frank-Keldysh modulators, acousto-optical modulators, Stark-effectmodulators, magneto-optical modulators, thermo-optical modulators,liquid crystal modulators, quantum-confinement optical modulators, andphotonic crystal modulators, among other possible types of modulators.

Encoder 4-104 generates modulating signal 4-105 based on the real numberto be encoded. For example, in some embodiments, encoder 4-104 mayinclude a table mapping real numbers to amplitudes for the modulatingsignal.

Optical transformation unit 4-108 may be configured to transform theintensity and/or the phase of the received optical field. For example,optical transformation unit 4-108 may include an optical fiber, anoptical waveguide, an optical attenuator, an optical amplifier (such asan erbium-doped fiber amplifier), a beam splitter, a beam combiner, amodulator (such as an electro-optic modulator, a Franz-Keldyshmodulator, a resonant modulator, or a Mach Zehnder modulator, amongothers), a phase shifter (such as a thermal phase shifter or a phaseshifter based on the plasma dispersion effect), an optical resonator, alaser, or any suitable combination thereof. In the context of opticalcommunications, optical transformation unit 4-108 may include a fiberoptic communication channel. The optical communication channel mayinclude for example optical fibers, and optionally, optical repeaters(e.g., erbium-doped fiber amplifiers). In the context of opticalprocessing, optical transformation unit 4-108 may include a photonicprocessing unit, an example of which is discussed in detail furtherbelow. In some embodiments, optical transformation unit 4-108 implementsa real transformation, such that the imaginary part of thetransformation is substantially equal to zero.

The optical field output by optical transformation unit 4-108 isprovided, directly or indirectly (e.g., after passing through one ormore other photonic components), to coherent receiver 4-110. Coherentreceiver 4-110 may include a homodyne optical receiver or a heterodyneoptical receiver. The reference signal with which the received signal isbeaten may be provided by local oscillator 4-112, as shown in FIG. 4-2A,or may be provided together with the modulated optical field afterpassing through optical transformation unit 4-108. Decoder 4-114 may bearranged to extract a real number (or a vector of real numbers) from thesignal output by coherent receiver 4-112.

In at least some of the embodiments in which photonic processing system1-100 of FIG. 1-1 implements encoding techniques of the types describedherein, optical modulator 4-106 may be part of optical encoder 1-101,optical transformation unit 4-108 may be part of photonic processor1-103 and coherent receiver 4-110 may be part of optical receiver 1-105.

FIG. 4-2B is a flowchart illustrating a method for processing realnumbers in the optical domain, in accordance with some embodiments.Method 4-150 may be performed using the system of FIG. 4-2A, or usingany other suitable system. Method 4-150 begins at act 4-152, in which avalue representative of a real number is provided. The real number maybe signed (i.e., may be positive or negative) in some embodiments. Thereal number may represent a certain environmental variable or parameter,such as a physical condition (e.g., temperature, pressure, etc.),information associated with an object (e.g., position, motion, velocity,rate of rotation, acceleration, etc.), information associated with amultimedia file (e.g., acoustic intensity of audio files, pixel colorand/or intensity of image or video files), information associated with acertain chemical/organic element or compound (e.g., concentration),information associated with financial assets (e.g., price of a certainsecurity), or any other suitable type of information includinginformation derived from the examples described above. The informationrepresented by the signed, real number may be useful for a variety ofreasons, including for example to train a machine learning algorithm, toperform forecasting, data analytics, troubleshooting, or simply tocollect data for future use.

At act 4-154, the value representative of the real number may be encodedonto an optical field. In some embodiments, encoding the value onto anoptical field involves modulating the phase and the intensity of anoptical field based on the value. As a result, the phase and theamplitude of the optical field reflect the encoded value. Act 4-154 maybe performed in some embodiments using encoder 4-104 and modulator 4-106(see FIG. 4-2A). In some such embodiments, modulating the phase andamplitude based on the value involves driving a single modulator with asingle electrical modulating signal. Thus, a single modulating signalmodulates both the phase and the amplitude of an optical field.Referring back to FIG. 4-2A by way of example and not limitation,encoder 4-104 may drive optical modulator 4-106 using a singlemodulating signal 4-105.

It should be noted that the fact that a single modulating signal is usedto drive the modulator does not preclude the use of other controlsignals for controlling the environment in which the modulator operates.For example, one or more control signals may be used to control thetemperature of the modulator or the temperature of a certain portion ofthe modulator. One or more control signals may be used to power theoperations of the modulator. One or more control signals may be used tobias the modulator in a certain regime of operation, such as to bias themodulator in its linear region or to set the wavelength of the modulatorto match the wavelength of the optical source (e.g., λ₀ of FIG. 4-2A).

A specific type of modulator is illustrated in FIG. 4-3A, in accordancewith some embodiments. Modulator 4-206 is a ring resonant modulator. Itshould be noted that ring modulators are described herein merely by wayof example, as any other suitable type of modulator, including thoselisted above, may alternatively be used. Modulator 4-206 includes awaveguide 4-208, a ring 4-210, and a phase shifter 4-212. Ring 4-210exhibits a resonant wavelength, the value of which depends, among otherparameters, the length of the ring's perimeter. When an optical fieldψ_(in) is launched into waveguide 4-208, the optical field may or maynot couple to ring 4-210 depending on the wavelength of ψ_(in) relativeto the resonant wavelength of ring 4-210. For example, if the wavelengthof ψ_(in) matches the resonant wavelength, at least part of the energyof ψ_(in) is transferred, via evanescent coupling, to ring 4-210. Thepower transferred to the ring will oscillate indefinitely inside thering until it is completely scattered or otherwise dissipated. Bycontrast, if the wavelength of ψ_(in) does not match the resonantwavelength, ψ_(in) may proceed straight through waveguide 4-208 withoutany significant attenuation.

Thus, the optical field may vary depending on the wavelength of ψ_(in)relative to the resonant wavelength. More particularly, the phase andthe intensity of the optical field may vary depending on the wavelengthof ψ_(in) relative to the resonant wavelength. FIG. 4-3B is a plotillustrating how the intensity (top chart) and the phase (bottom chart)may vary depending on the wavelength of ψ_(in) relative to the resonantwavelength. The top chart illustrates the intensity spectral response aas a function of all possible wavelengths (λ) that ω_(in) may have. Thebottom chart illustrates the phase spectral response θ as a function ofthe same wavelength. At the resonant frequency, the intensity responseexhibits a dip and the phase response exhibits an inflection point.Wavelengths substantially lower that the resonant frequency are subjectto a low intensity attenuation (α˜1) and a low phase change (θ˜0).Wavelengths substantially greater that the resonant frequency aresubject to a low intensity attenuation (α˜1) and a change in sign(θ˜−π). At the resonant wavelength, the intensity of ψ_(in) isattenuated by the value corresponding to the dip and is subject to aphase shift equal to the value of the inflection point (i.e., −π/2). Inthe example of FIG. 4-3B, the wavelength (λ₀) of ψ_(in) is slightlyoffset relative to the resonant frequency. In this case, the intensityof ψ_(in) is attenuated by α_(v0) (where 0<α_(v0)<1) and the phase isshifted by θ_(v0) (where −π<θ_(v0)<−π/2).

Referring back to FIG. 4-3A, modulation of the amplitude and phase ofψ_(in) may be performed using voltage V, which in this case embodiesmodulating signal 4-105 of FIG. 4-2A. When a voltage V is applied tophase shifter 4-212, the refractive index of the phase shifter changesand so does, as a result, the effective length of the ring's perimeter.This, in turn, leads to a variation in the resonant frequency of thering. The extent to which the effective perimeter length (andaccordingly, the resonant frequency) varies depends upon the amplitudeof V.

In the example of FIG. 4-3B, voltage V is set to zero. In the example ofFIG. 4-3C, the voltage is set to V₁. As shown in FIG. 4-3C, applicationof V₁ to phase shifter 4-212 leads to a shift in the intensity and phasespectral responses of the modulator along the wavelength axis. In thiscase, the responses exhibit a redshift (i.e., a shift towards greaterwavelengths). The result is that the intensity of ψ_(in) is nowattenuated by α_(v1) (α_(v1) being different than α_(v0)) and the phaseis shifted by θ_(v1) (θ_(v1) being different than θ_(v0)).

Thus, varying voltage V leads to a change in intensity and phase ofψ_(in). In other words, V may be viewed as a modulating signal. In someembodiments, voltage V may be the only modulating signal drivingmodulator 4-210. Voltage V may assume the following expression in someembodiments V=V_(DC)+V(t), where V_(DC) is a constant and V(t) is variedover time depending on the real numbers to encode. V_(DC) may pre-biasthe ring so that resonant frequency is in proximity to the wavelength(λ₀) of ψ_(in).

FIG. 4-3D is an encoder table (e.g., a look-up table) providing anexample of how real numbers may be encoded, using modulator 4-206, intothe phase and intensity of an optical field, in accordance with someembodiments. In this case it will be assumed, by way of example, thatreal numbers between −10 and 10 with increments equal to 1 are to beencoded in the optical domain (see column labeled “real number toencode”). Of course, any suitable set of real numbers may be encodedusing the techniques described herein. The column labeled “V (appliedvoltage)” indicates the voltage being applied to phase shifter 4-212.The column labeled “a (amplitude modulation)” indicates the value of theintensity spectral response at the wavelength (λ₀) of ψ_(in) (see FIGS.4-3B and 4-3C, top charts). The column labeled “θ (phase modulation)”indicates the value of the phase spectral response at the wavelength(λ₀) of ψ_(in) (see FIGS. 4-3B and 4-3C, bottom charts). In this case,real number “−10” is mapped to a certain voltage V₁, which leads to anamplitude modulation α₁ and a phase modulation θ₁; real number “−9” ismapped to a certain voltage V₂, which leads to an amplitude modulationα₂ and a phase modulation θ₂; and so on. Real number “10” is mapped to acertain voltage V_(N), which leads to an amplitude modulation α_(N) anda phase modulation θ_(N). Thus, different real numbers are encoded withdifferent intensity/phase pairs.

FIG. 4-3E provides a visual representation, in the complex plane, of theencoding table of FIG. 4-3D, in accordance with some embodiments.Different points along the line 4-300 represent differentintensity/phase pairs, in accordance with the spectral responses ofFIGS. 4-3B and 4-3C, as the voltage is varied. The symbol S_(in), forexample, represents a specific symbol characterized by intensitymodulation a and a phase modulation θ. S_(in) represents a certain rowfrom the table of FIG. 4-3D. Each row of the table is mapped on adifferent point of line 4-300.

Referring back to FIG. 4-2B, at act 4-156, a transformation is appliedto the modulated optical signal output by the modulator. Thetransformation may involve an intensity transformation and/or a phasetransformation, depending on the way one desires to process the encodedreal numbers. In some embodiments, optical transformation unit 4-108 maybe used for the transformation of the signal output by modulator 4-106.Optical transformation unit 4-108 may be configured to transform asymbol S_(in), which is characterized by an intensity α and phase θ,into a symbol S_(out), which is characterized by an intensity αβ andphase θ+Δθ. In other words, optical transformation unit 4-108 introducesan intensity modulation β and a phase shift Δθ. The values of β and Δθdepend upon the specific optical transformation unit being used.

Considering by way of example the input symbol S_(in) of FIG. 4-3E,FIGS. 4-4A through 4-4C provide examples of how the opticaltransformation unit may perform the transformation. The transformationof FIG. 4-4A is such that β<1 and Δθ=0. That is, the opticaltransformation unit introduces an attenuation but does not alter thephase of the input optical field. This may be the case, for example, ofan optical fiber with no optical repeaters, the optical fiber having alength selected to maintain the input phase. The result is that line4-300 is transformed to line 4-401, where line 4-401 is a compressedversion of line 4-300. The output sample, S_(out), has the same phase asthe S_(in), but the intensity is equal to αβ (i.e., is attenuated by β).

The transformation of FIG. 4-4B introduces an attenuation as well as aphase shift. This may be the case, for example, of an optical fiber withno optical repeaters, the optical fiber having an arbitrary length. Theresult is that line 4-300 is transformed to line 4-402, where line 4-402is a compressed and rotated version of line 4-300. The output sample,S_(out), has phase θ+Δθ and intensity αβ.

The transformation of FIG. 4-4C involves a multi-mode transformation.This may occur when the input optical field is combined with one or moreother optical fields. With multi-mode transformations, line 4-403 can bere-shaped in any suitable manner. Examples of multi-mode transformationsinclude optical processing units, some of which are described in detailfurther below.

Referring back to FIG. 4-2B, at act 4-158, a coherent receiver may mixthe transformed, modulated optical field with a reference optical signalto obtain an electric output signal. In some embodiments, the mixing maybe performed using coherent receiver 4-110 of FIG. 4-2A. The referenceoptical signal may be a signal generated by a local oscillator (e.g.,local oscillator 4-112 of FIG. 4-2A), or may be a signal transmittedtogether with the modulated optical field through the opticaltransformation unit. The effect of the mixing is illustrated in FIG. 4-5by way of example and not limitation. Line 4-403 (the same line shown inFIG. 4-4C) represents all possible intensity/phase pairs coming out of acertain multi-mode transformation. FIG. 4-5 may depict, for example, theeffect of propagating an optical signal through photonic processor 1-103of FIG. 1-1.

Suppose that S_(out) is the symbol coming out of the transformation at acertain time. As described above, symbol S_(out) is characterized by anintensity αβ and a phase θ+Δθ. When the transformed field is mixed withthe reference signal, points along line 4-403 are projected on areference axis 4-500. Thus, for example, symbol S_(out) is projectedonto point A on the axis 4-500. Point A represents the electric outputsignal arising out of the mixing, and is characterized by an amplitudeequal to the distance between 0 and A (OA). It should be noted that theangle of the axis 4-500 relative to the real axis (i.e., angle φ),depends upon the phase of the reference signal relative to thetransformed, modulated optical field. In this example, 0<φ<π/4.

Referring back to FIG. 4-2B, at act 4-160, a decoder may obtain a valuerepresentative of a decoded real number based on the electric outputsignal obtained at act 4-158. In some embodiments, when decoding symbolsobtained through an optical transformation, the exact location of thesymbol in the complex plane may be unimportant. What matters foraccurate decoding, according to some embodiments, is the projection ofthe optical signal on a known reference axis. In other words, symbolsalong the line 4-403 can be mapped to points along the reference axis,much like symbol S_(out) is mapped to point A. Thus, some embodimentsimplement optical decoding schemes in which points along a referenceaxis correspond to specific symbols in the complex plane.

The manner in which points along the reference axis are mapped tosymbols in the complex plane may be determined using a calibrationprocedure. During the calibration procedure, a set of input symbols ofknown intensity and phase (the symbols representative of a set of realnumbers), are passed through a certain optical transformation unit, andthe resulting symbols are coherently detected using a reference signalhaving a known phase. The amplitude of the resulting electric outputsignal (i.e., the amplitude of the projection along the reference axis)is recorded and stored in a table (e.g., a look-up table). The table maysubsequently be used during operation to decode real numbers based onthe amplitude of the projection along the reference axis.

An example of such a table is illustrated in FIG. 4-6, in accordancewith some embodiments. This table includes a column for the phase of thereference signal (“reference phase φ”), a column for the projectionalong the reference axis (“projection OA,” i.e., the amplitude of theelectrical output signal), and a column for the decoded real number(“decoded real number”). The table may be populated during a calibrationprocedure involving 1) passing a symbol of known intensity and phasethrough a known optical transformation unit, 2) setting the phase of areference signal to a known value, 3) mixing the transformed symbol withthe reference signal thereby projecting the transformed symbol onto theknown reference axis, 4) determining the amplitude of the projectionalong the reference axis, and 5) recording the expected output realnumber. During operation, a user may infer the decoded real number usingthe table based on the value of the reference phase and the measuredprojection.

In the example of FIG. 4-6, only two reference phases are considered byway of example and not limitation—0 and π/6. At φ=0, projections −1,−0.8 and 1 are mapped to real numbers −9.6, 0.2 and 8.7, respectively.At φ=π/6, projections −0.4, 0.6 and 0.9 are mapped to real numbers 3.1,−5 and 10, respectively. Thus, for example, if a user obtains aprojection of −0.4 when using a reference phase of π/6, the user caninfer that the decoded real number is 3.1.

FIG. 4-7 illustrates a specific example of a photonic system 4-700 inwhich the optical transformation unit includes an optical fiber 4-708,in accordance with some embodiments. This example illustrates howencoding techniques of the types described herein may be used in thecontext of optical communications. In this implementation, optical fiber4-708 separates transmitter 4-701 from receiver 4-702.

Transmitter 4-701 includes optical source 4-102, encoder 4-104 andoptical modulator 4-106 (described above in connection with FIG. 4-2A)and receiver 4-702 includes coherent receiver 4-110 and decoder 4-114(also described above in connection with FIG. 4-2A). As described above,in some embodiments, optical modulator 4-106 may be driven using asingle modulating signal 4-105. In some such embodiments, opticalmodulator 4-106 may implement an on-off keying (OOK) modulation scheme.

It should be noted that, in this implementation, the reference signalused for the coherent detection is provided directly from transmitter4-701, rather than being generated locally at receiver 4-702 (thoughother implementations may involve local oscillators at receiver 4-702).To illustrate this concept, consider for example the plot of FIG. 4-8.FIG. 4-8 illustrates an example of a power spectral density of anoptical field at the output of transmitter 4-701. As shown, the powerspectral density includes a signal 4-800 as well as a carrier 4-801.Signal 4-800 represents the information encoded using modulator 4-106.Carrier 4-801 represents a tone at the wavelength of optical source4-102 (λ₀). Upon transmission of this optical field through opticalfiber 4-708, coherent receiver 4-110 may perform the mixing usingcarrier 4-801 itself as the reference signal.

Some embodiments relate to methods for fabricating photonic systems ofthe types described herein. One such method is depicted in FIG. 4-9, inaccordance with some embodiments. Method 4-900 begins at act 4-902, inwhich a modulator is fabricated. The modulator may be fabricated to bedriven with a single electrical modulating signal. An example of amodulator is modulator 4-106 (FIG. 4-2A). At act 4-904, a coherentreceiver is fabricated. In some embodiments, the coherent receiver isfabricated on the same semiconductor substrate on which the modulator isfabricated. At act 4-906, an optical transformation unit is fabricated.The optical transformation unit may be fabricated on the same substrateon which the coherent receiver is fabricated and/or on the samesemiconductor substrate on which the modulator is fabricated. Theoptical transformation unit may be fabricated to be coupled between themodulator and the coherent receiver. An example of an opticaltransformation unit is optical transformation unit 4-108 (FIG. 4-2A).

V. Differential Receiver

The inventors have recognized and appreciated that some conventionaloptical receivers are particularly susceptible to noise generated fromvoltage supplies, to noise arising from the fact that photodetectorsinevitably produce dark currents, and to other forms of noise. Thepresence of noise reduces the signal-to-noise ratio, and therefore, theability of these photodetectors to accurately sense incoming opticalsignals. This can negatively affect the performance of the system inwhich these photodetectors are deployed. For example, this cannegatively affect the system's bit error rate and power budget.

The inventors have developed optical receivers with reducedsusceptibility to noise. Some embodiments of the present application aredirected to optical receivers in which both the optical-to-electricconversion and the amplification are performed in a differentialfashion. In the optical receivers described herein, two separate signalsubtractions take place. First, the photocurrents are subtracted fromone another to produce a pair of differential currents. Then, theresulting differential currents are further subtracted from each otherto produce an amplified differential output. The inventors haverecognized and appreciated that having an optical receiver involvingmultiple levels of signal subtraction results in multiple levels ofnoise cancelation, thus substantially reducing noise from the system.This can have several advantages over conventional optical receivers,including wider dynamic range, greater signal-to-noise ratio, largeroutput swing, and increased supply-noise immunity.

Optical receivers of the types described herein can be used in a varietyof settings, including for example in telecom and datacom (includinglocal area networks, metropolitan area networks, wide area networks,data center networks, satellite networks, etc.), analog applicationssuch as radio-over-fiber, all-optical switching, Lidar, phased arrays,coherent imaging, machine learning and other types of artificialintelligence applications, as well as other applications. In someembodiments, optical receivers of the types described herein may be usedas part of a photonic processing system. For example, in someembodiments, optical receivers of the types described herein may be usedto implement optical receiver 1-105 of FIG. 1-1 (e.g., one or morehomodyne receivers 1-901 of FIG. 1-9).

FIG. 5-1 illustrates a non-limiting example of an optical receiver5-100, in accordance with some non-limiting embodiments of the presentapplication. As shown, optical receiver 5-100 includes photodetectors5-102, 5-104, 5-106 and 5-108, though other implementations include morethan four photodetectors. Photodetector 5-102 may be connected tophotodetector 5-104, and photodetector 5-106 may be connected tophotodetector 5-108. In some embodiments, the anode of photodetector5-102 is connected to the cathode of photodetector 5-104 (at node5-103), and the cathode of photodetector 5-106 is connected to the anodeof photodetector 5-108 (at node 5-105). In the example of FIG. 5-1, thecathodes of photodetectors 5-102 and 5-108 are connected to voltagesupply V_(DD) and the anodes of photodetectors 5-104 and 5-106 areconnected to the reference potential (e.g., to ground). The oppositearrangement is also possible in some embodiments. The referencepotential may be at a potential equal to zero or having any suitablevalue, such as −V_(DD). V_(DD) may have any suitable value.

Photodetectors 5-102 through 5-108 may be implemented in any of numerousways, including for example with pn-junction photodiodes, pin-junctionphotodiodes, avalanche photodiodes, phototransistors, photoresistors,etc. The photodetectors may include a material capable of absorbinglight at the wavelength of interest. For example, at wavelengths in theO-band, C-band or L-band, the photodetectors may have an absorptionregion made at least in part of germanium, by way of a non-limitingexample. For visible light, the photodetectors may have an absorptionregion made at least in part of silicon, by way of another non-limitingexample.

Photodetectors 5-102 through 5-108 may be integrated components formedmonolithically as part of the same substrate. The substrate may be asilicon substrate in some embodiments, such as a bulk silicon substrateor a silicon-on-insulator. Other types of substrates can also be used,including for example indium phosphide or any suitable semiconductormaterial. To reduce variability in the characteristics of thephotodetectors due to fabrication tolerances, in some embodiments, thephotodetectors may be positioned in close proximity to one another. Forexample, the photodetectors may be positioned on a substrate within anarea of 1 mm² or less, 0.1 mm² less or 0.01 mm² or less.

As further illustrated in FIG. 5-1, photodetectors 5-102 through 5-108are connected to a differential operational amplifier 5-110. Forexample, photodetectors 5-102 and 5-104 may be connected to thenon-inverting input (“+”) of DOA 5-110 and photodetectors 5-106 and5-108 may be connected to the inverting input (“−”) of DOA 5-110. DOA5-110 has a pair of outputs. One output is inverting and one output isnon-inverting.

In some embodiments, as will be described in detail in connection withFIG. 5-2, photodetectors 5-102 and 5-106 may be arranged to receive thesame optical signal “t” and photodetectors 5-104 and 5-108 may bearranged to receive the same optical signal “b.” In some embodiments,photodetectors 5-102 through 5-108 may be designed to be substantiallyequal to each other. For example, photodetectors 5-102 through 5-108 maybe formed using the same process steps and using the same photomaskpatterns. In these embodiments, photodetectors 5-102 through 5-108 mayexhibit substantially the same characteristics, such as substantiallythe same responsivity (the ratio between the photocurrent and thereceived optical power) and/or substantially the same dark current (thecurrent generated when no optical power is received). In theseembodiments, the photocurrents generated by photodetectors 5-102 and5-106 responsive to reception of signal t may be substantially equal toeach other. Such photocurrents are identified as “i_(t)” in FIG. 5-1. Itshould be noted that, due to the orientations of photodetectors 5-102and 5-106, the photocurrents generated by photodetectors 5-102 and 5-106are oriented in opposite directions. That is, the photocurrent ofphotodetector 5-102 is directed towards node 5-103 and the photocurrentof photodetector 5-106 is oriented away from node 5-105. Furthermore,the photocurrents generated by photodetectors 5-104 and 5-108 responsiveto reception of signal b may be substantially equal to each other. Suchphotocurrents are identified as “i_(b).” Due to the orientations ofphotodetectors 5-104 and 5-108 relative to each other, the photocurrentsgenerated by photodetectors 5-104 and 5-108 are oriented in oppositedirections. That is, the photocurrent of photodetector 5-108 is directedtowards node 5-105 and the photocurrent of photodetector 5-104 isoriented away from node 5-103.

In view of the orientations of the photodetectors, a current withamplitude i_(t)−i_(b) emerges from node 5-103 and a current withamplitude i_(b)−i_(t) emerges from node 5-105. Thus, the currents havesubstantially the same amplitudes, but with opposite signs.

Photodetectors 5-102 through 5-108 may produce dark currents. Darkcurrents are typically due to leakage and arise from a photodetectorregardless of whether the photodetector is exposed to light or not.Because dark currents arise even in the absence of incoming opticalsignals, dark currents effectively contribute to noise in the opticalreceiver. The inventors have appreciated that the negative effects ofthese dark currents can be significantly attenuated thanks to thecurrent subtraction described above. Thus, in the example of FIG. 5-1,the dark current of photodetector 5-102 and the dark current ofphotodiode 5-104 substantially cancel out one another (or at least aremutually substantially reduced), and so do the dark currents ofphotodetector 5-106 and 5-108. Consequently, noise due to the presenceof the dark currents is greatly attenuated.

FIG. 5-2 illustrates a photonic circuit 5-200 arranged for providing twooptical signals to photodetectors 5-102 through 5-108, in accordancewith some non-limiting embodiments. Photonic circuit 5-200 may comprisesoptical waveguides for routing the optical signals to thephotodetectors. The optical waveguides may be made of a material that istransparent or at least partially transparent to light at the wavelengthof interest. For example, the optical waveguides be made of silicon,silicon oxide, silicon nitride, indium phosphide, gallium arsenide, orany other suitable material. In the example of FIG. 5-2, photoniccircuit 5-200 includes input optical waveguides 5-202 and 204 andcouplers 5-212, 5-214 and 5-216. As further illustrated, the outputoptical waveguides of photonic circuit 5-200 are coupled tophotodetectors 5-102 through 5-108.

In the example of FIG. 5-2, couplers 5-212, 5-214 and 5-216 comprisedirectional couplers, where evanescent coupling enables transfer ofoptical power between adjacent waveguides. However, other types ofcouplers may be used such as Y-junctions, X-junctions, opticalcrossovers, counter-direction couplers, etc. In other embodiments,photonic circuit 5-200 may be implemented with a multi-modeinterferometer (MMI). Couplers 5-212, 5-214 and 5-216 may be 3 dBcouplers (with a 50%-50% coupling ratio) in some embodiments, thoughother ratios are also possible, such as 51%-49%, 55%-45% or 60%-40%. Itshould be appreciated that, due to fabrication tolerances, the actualcoupling ratio may deviate slightly from the intended coupling ratio.

Signal s₁ may be provided at input optical waveguide 5-202 and signal s₂may be provided at input optical waveguide 204. Signals s₁ and s₂ may beprovided to the respective input optical waveguides using for exampleoptical fibers. In some embodiments, s₁ represents a reference localoscillator signal, such as the signal generated by a reference laser,and s₂ represents the signal to be detected. As such, the opticalreceiver may be viewed as a homodyne optical receiver. In some suchembodiments, s₁ may be a continuous wave (CW) optical signal while s₂may be modulated. In other embodiments, both signals are modulated orboth signals are CW optical signals, as the application is not limitedto any particular type of signal.

In the example of FIG. 5-2, signal s₁ has amplitude A_(LO) and phase ϑ,and signal s₂ has amplitude A_(S) and phase φ. Coupler 5-212 combinessignals s₁ and s₂ such that signals t and b emerge at respective outputsof coupler 5-212. In the embodiments in which coupler 5-212 is a 3 dBcoupler, t and b may be given by the following expression:

$\left( \frac{t}{b} \right) = {\frac{1}{\sqrt{2}}\left( {1\mspace{14mu} i\mspace{14mu} i\mspace{14mu} 1} \right)\left( \frac{A_{LO}e^{i\;\vartheta}}{A_{s}e^{i\;\varphi}} \right)}$

and the powers T and B (of t and b, respectively) may be given by thefollowing expressions:

T=[A _(LO) ² +A _(S) ²+2A _(LO) A _(S) sin(ϑ−φ)]

B=[A _(LO) ² +A _(S) ²−2A _(LO) A _(S) sin(ϑ−φ)]

Thus, in the embodiments in which couplers 5-214 and 5-216 are 3 dBcouplers, photodetectors 5-102 and 5-106 may each receive a power givenby T/2 and photodetectors 5-104 and 5-108 may each receive a power givenby B/2.

Referring back to FIG. 5-1, and assuming that the responsivities ofphotodetectors 5-102 through 5-108 are all equal to each other (thoughnot all embodiments are limited in this respect), the currents emergingfrom node 5-103 and 5-105, respectively, may be given by the followingexpressions:

i _(t) −i _(b)=2A _(LO) A _(S) sin(ϑ−φ)

i _(b) −i _(t)=−2A _(LO) A _(S) sin(ϑ−φ)

DOA 5-110 is arranged to amplify the differential signal received at the“+” and “−” inputs, and to produce an amplified differential output,represented in FIG. 5-1 by voltages V_(out,n) and V_(out,p). In someembodiments, DOA 5-110, in combination with impedances z, may be viewedas a differential transimpedance amplifier, in that it produces adifferential pair of voltage (V_(out,n), V_(out,p)) based on adifferential pair of current (i_(b)−i_(t), i_(t)−i_(b)). In someembodiments, each of V_(out,n), V_(out,p) may be proportional to thedifference between current i_(t)−i_(b) and current i_(b)−i_(t), thusgiving rise to the following expressions:

V _(out,p)=2z(i _(t) −i _(b))

V _(out,n)=2Z(i _(b) −i _(t))

This differential pair of voltages may be provided as input to anysuitable electronic circuit, including but not limited to ananalog-to-digital converter (not shown in FIG. 5-1). It should be notedthat optical receiver 5-100 provides two levels of noise rejection. Thefirst level of noise rejection occurs thanks to the subtraction of thephotocurrents, the second level of noise rejection occurs thanks to thesubtraction taking place in the differential amplification stage. Thisresults in a significant increase in noise rejection.

In the example of FIG. 5-1, impedances z are shown as being equal toeach other, however different impedances may be used in otherembodiments. These impedances may include passive electric components,such as resistors, capacitors and inductors, and/or active electroniccomponents, such as diode and transistors. The components constitutingthese impedances may be chosen to provide a desired gain and bandwidth,among other possible characteristics.

As discussed above, optical receiver 5-100 may be integratedmonolithically on a substrate. One such substrate is illustrated in FIG.5-3A, in accordance with some non-limiting embodiments. In this example,photodetectors 5-102 through 5-108, photonic circuit 5-200 and DOA 5-110are monolithically integrated as part of substrate 5-301. In otherembodiments, photodetectors 5-102 through 5-108 and photonic circuit5-200 may be integrated on substrate 5-301 and DOA 5-110 may beintegrated on a separate substrate 5-302. In the example of FIG. 5-3B,substrates 5-301 and 5-302 are flip-chip bonded to one another. In theexample of FIG. 5-3C, substrates 5-301 and 5-302 are wire bonded to oneanother. In yet another example (not illustrated), photodetectors 5-102through 5-108 and photonic circuit 5-200 may be fabricated on separatesubstrates.

Some embodiments of the present application are directed to methods forfabricating optical receivers. One such method is depicted in FIG. 5-4,in accordance with some non-limiting embodiments. Method 5-400 begins atact 5-402, in which a plurality of photodetectors are fabricated on afirst substrate.

Once fabricated, the photodetectors may be connected together, forexample in the arrangement shown in FIG. 5-1. In some embodiments, thephotodetectors may be positioned on the first substrate within an areaof 1 mm² or less, 0.1 mm² less or 0.01 mm² or less. At act 5-404, aphotonic circuit is fabricated on the first substrate. The photoniccircuit may be arranged to provide a pair of optical signals to thephotodetectors, for example in the manner shown in FIG. 5-2. At act5-406, a differential operational amplifier may be fabricated on asecond substrate. An example of a differential operational amplifier isDOA 5-110 of FIG. 5-1. At act 5-408, the first substrate may be bondedto the second substrate, for example via flip-chip bonding (as shown inFIG. 5-3A), wire bonding (as shown in FIG. 5-3B), or using any othersuitable bonding technique. Once the substrates are bonded, thephotodetectors of the first substrate may be electrically connected tothe differential operational amplifier of the second substrate, forexample in the manner shown in FIG. 5-1.

Examples of fabrication processes are depicted schematically at FIGS.5-4A through 5-4F, in accordance with some embodiments. FIG. 5-4Adepicts a substrate 5-301 having a lower cladding 5-412 (e.g., an oxidelayer such as a buried oxide layer or other types of dielectricmaterials) and a semiconductor layer 5-413 (e.g., a silicon layer or asilicon nitride layer, or other types of material layers). At FIG. 5-4B,semiconductor layer 5-413 is patterned, for example using aphotolithographic exposure, to form regions 5-414. Regions 5-414 may bearranged to form optical waveguides in some embodiments. In someembodiments, the resulting pattern resembles photonic circuit 5-200(FIG. 5-2), where waveguides 5-202 and 204, and couplers 5-212, 5-214and 5-216 are embedded into one or more regions 5-414. At FIG. 5-4C,photodetectors 5-102, 5-104, 5-106 and 5-108 (and optionally, otherphotodetectors) are formed. In this example, an optical absorbingmaterial 5-416 is deposited to be adjacent a region 5-414. The opticalabsorbing material 5-416 may be patterned to form the photodetectors.The material used for the optical absorbing material may depend on thewavelength to be detected. For example, germanium may be used forwavelengths in the L-Band, C-Band or O-Band. Silicon may be used forvisible wavelengths. Of course, other materials are also possible. Theoptical absorbing material 5-416 may be positioned to be opticallycoupled to regions 5-414 in any suitable way, including but not limitedto butt coupling, taper coupling and evanescent coupling.

At FIG. 5-4D, DOA 5-110 is formed. In some embodiments, DOA 5-110includes several transistors formed via ion implantation. FIG. 5-4Ddepicts implanted regions 5-418, which may form part of one or moretransistors of DOA 5-110. While only one ion implantation is illustratedin FIG. 5-4D, in some embodiments, formation of DOA 5-110 may involvemore than one ion implantations. Additionally, DOA 5-110 may beelectrically connected to the photodetectors, for example via one ormore conductive traces formed on substrate 5-301.

The arrangement of FIG. 5-4D is such that photonic circuit 5-200,photodetectors 5-102 through 5-108 and DOA 5-110 are formed on a commonsubstrate (as shown in FIG. 5-3A). Arrangements in which DOA 5-110 isformed on a separate substrate (as shown in FIG. 5-3B or FIG. 5-3C) arealso possible. In one such example, DOA 5-110 is formed on a separatesubstrate 5-302, as shown in FIG. 5-4E, where implanted regions 5-428are formed via one or more ion implantations.

Subsequently, substrate 5-301 is bonded to substrate 5-302, andphotodetectors 5-102 through 5-108 are connected to DOA 5-110. At FIG.5-4F, conductive pads 5-431 are formed and placed in electricalcommunication with optical absorbing material 5-416, and conductive pads5-432 are formed and placed in electrical communication with implantedregions 5-428. The conductive pads are electrically connected via wirebonding (as shown in FIG. 5-4F) or via flip-chip bonding.

Some embodiments are directed to methods for receiving input opticalsignals. Some such embodiments may involve homodyne detection, thoughthe application is not limited in this respect. Other embodiments mayinvolve heterodyne detection. Yet other embodiments may involve directdetection. In some embodiments, reception of optical signals may involveoptical receiver 5-100 (FIG. 5-1), though other types of receivers maybe used.

An example of a method for receiving an input optical signal is depictedin FIG. 5-5, in accordance with some embodiments. Method 5-500 begins atact 5-502, in which the input signal is combined with a reference signalto obtain first and second optical signals. The input signal may beencoded with data, for example in the form of amplitude modulation,pulse width modulation, phase or frequency modulation, among other typesof modulation. In some of the embodiments involving homodyne detection,the reference signal may be a signal generated by a local oscillator(e.g., a laser). In other embodiments, the reference signal may also beencoded with data. In some embodiments, the input signal and thereference signal are combined using a photonic circuit 5-200 (FIG. 5-2),though other types of optical combiners may be used, including but notlimited to MMIs, Y-junctions, X-junctions, optical crossovers, andcounter-direction couplers. In the embodiments in which photonic circuit5-200 is used, t and b may represent the signals obtained from thecombination of the input signal with the reference signal.

At act 5-504, the first optical signal is detected with a firstphotodetector and with a second photodetector and the second opticalsignal is detected with a third photodetector and with a fourthphotodetector to produce a pair of differential currents. In someembodiments, act 5-504 may be performed using optical receiver 5-100(FIG. 5-1). In some such embodiments, the first optical signal isdetected with photodetectors 5-102 and 5-106, and the second opticalsignal is detected with photodetectors 5-104 and 5-108. The producedpair of differential currents is represented, collectively, by currentsi_(b)−i_(t) and i_(t)−i_(b). Being differential, in some embodiments,the currents of the pair may have substantially equal amplitudes, butwith substantially opposite phases (e.g., with a n-phase difference).

At act 5-506, a differential operational amplifier (e.g., DOA 5-110 ofFIG. 5-1) produces a pair of amplified differential voltages using thepair of differential currents produced at act 5-504. In the embodimentsthat use DOA 5-110, the produced pair of differential voltages isrepresented by voltages V_(out,n) and V_(out,p). Being differential, insome embodiments, the voltages of the pair may have substantially equalamplitudes, but with substantially opposite phases (e.g., with a π-phasedifference).

Method 5-500 may have one or more advantages over conventional methodsfor receiving optical signals, including for example wider dynamicrange, greater signal-to-noise ratio, larger output swing, and increasedsupply-noise immunity.

VI. Phase Modulator

The inventors have recognized and appreciated that certain optical phasemodulators suffer from high dynamic loss and low modulation speed, whichsignificantly limit the range of applications in which these phasemodulators can be deployed. More specifically, some phase modulatorsinvolve significant trade-offs between modulation speed and dynamicloss, such that an increase in modulation speed results in an increasein dynamic loss. As used herein, the phrase “dynamic loss” refers tooptical power loss experienced by an optical signal that depends on thedegree to which its phase is modulated. Ideal phase modulators are suchthat power loss is independent of the phase modulation. Real-world phasemodulators, however, experience a certain power loss when no modulationoccurs, and experience a different power loss when modulation occurs.For example, the power loss experienced at no phase modulation may beL₁, the power loss experienced at a π/2-phase modulation may be L₂, andthe power loss experienced at a π-phase modulation may be L₃, with L₁,L₂ and L₃ being different from each other. This behavior is undesirablebecause, in addition to phase modulation, the signal further experiencesamplitude modulation.

Some such phase modulators, in addition, require several hundreds ofmicrons in length to provide sufficiently large phase shifts.Unfortunately, being so long, such phase modulators are not suitable foruse in applications requiring integration of several phase shifters on asingle chip. The phase modulators alone may take up most of the spaceavailable on the chip, thus limiting the number of devices that can beco-integrated on the same chip.

Recognizing the aforementioned limitations of certain phase modulators,the inventors have developed small footprint-optical phase modulatorscapable of providing high modulation speeds (e.g., in excess of 6-100MHz or 1 GHz) while limiting dynamic loss. In some embodiments, a phasemodulator may occupy an area as small as 300 μm². Thus, as an example, areticle having an area of 1 cm² can accommodate as many as 15,000 phasemodulators while saving an additional 50 mm² for other devices.

Some embodiments relate to Nano-Opto-Electromechanical Systems (NOEMS)phase modulators having multiple suspended optical waveguides positionedadjacent to one another and forming a plurality of slots therebetween.The dimensions of the slots are sufficiently small to form slotwaveguides, whereby a substantial portion (e.g., a majority) of the modeenergy is confined in the slots themselves. These modes are referred toherein as slot modes. Having a substantial portion of the mode energy inthe slots enables modulation of the effective index of the mode, and aresult, of the phase of an optical signal embodying the mode, by causingvariations in the dimensions of the slots. In some embodiments, phasemodulation can be achieved by applying mechanical forces that causevariations in the dimensions of the slots.

The inventors have recognized and appreciated that the modulation speedachievable with the NOEMS phase modulators described herein can beincreased, without significant increases in dynamic loss, by decouplingthe mechanical drivers from the region where optical modulation takesplace. In phase modulators in which the mechanical drivers are decoupledfrom the optical modulation region, electric driving signals are appliedon the mechanical drivers, rather than being applied on the opticalmodulation region itself. This arrangement removes the need to make theoptical modulation region electrically conductive, thus enabling areduction in the doping of this region. The low doping results in areduction of free carriers which may otherwise lead to opticalabsorption, thus lowering dynamic loss.

Furthermore, decoupling the mechanical drivers from the opticalmodulation region enables a greater modulation per unit length, and as aresult a shorter modulation region. Shorter modulation regions, in turn,enable, greater modulation speed.

The inventors have further recognized and appreciated that includingmultiple slots in the modulation region can enable a further reductionin the length of the phase modulator (thereby decreasing its size).Having more than one slot, in fact, enables a substantial reduction inthe length of the transition region through which light is coupled tothe modulation region. The result is a substantially more compact formfactor. Thus, NOEMS phase modulators of the types described herein canhave shorter modulation regions and/or shorter transition regions. Phasemodulators of the types described herein can have lengths as low as 20μm or 30 μm, in some embodiments.

As will be described in detail further below, some embodiments relate tophase modulators in which a trench is formed in the chip, and isarranged so that the modulating waveguides are suspended in air and arefree move in space.

The inventors have recognized a potential drawback associated with theuse of trenches that results from the formation of cladding/airinterfaces. When a propagating optical signal enters (or exits) atrench, it encounters a cladding/air interface (or an air/claddinginterface). Unfortunately, the presence of the interface can give riseto optical reflections, which in turn can increase insertion losses. Theinventors have appreciated that the negative effect of such interfacescan be mitigated by reducing the physical extension of the optical modein the region where it passes through the interface. This can beachieved in various ways. For example, in some embodiments, theextension of the optical mode may be reduced by tightly confining themode within a rib waveguide. A rib waveguide may be sized so that only asmall fraction of the mode energy (e.g., less than 20%, less than 10%,or less than 5%) is outside the edges of the waveguide.

NOEMS phase modulators of the types described herein may be used in avariety of applications, including for example in telecom and datacom(including local area networks, metropolitan area networks, wide areanetworks, data center networks, satellite networks, etc.), analogapplications such as radio-over-fiber, all-optical switching, coherentLidar, phased arrays, coherent imaging, machine learning and other typesof artificial intelligence applications. Additionally, the NOEMSmodulators may be used as part of amplitude modulators, for example ifcombined with a Mach Zehnder modulator. For example, a Mach Zehndermodulator may be provided in which a NOEMS phase modulator is positionedin one or more of the arms of the Mach Zehnder modulator. Severalmodulation schemes may be enabled using NOEMS pjhase modulators,including for example amplitude shift keying (ASK), quadrature amplitudemodulation (QAM), phase shift keying (BPSK), quadrature phase shiftkeying (QPSK) and higher order QPSK, offset quadrature phase-shiftkeying (OQPSK), Dual-polarization quadrature phase shift keying(DPQPSK), amplitude phase shift keying (APSK), etc. Additionally, NOEMSphase modulators may be used as phase correctors in applications inwhich the phase of an optical signal tends to drift unpredictably. Insome embodiments, NOEMS phase modulators of the types described hereinmay be used as part of a photonic processing system. For example, insome embodiments, NOEMS phase modulators of the types described hereinmay be used to implement phase modulators 1-207 of FIG. 1-1, and/or toimplement part of the variable beam splitters 1-401 of FIG. 104, and/orto implement the phase shifters 1-505, 1-507 and 1-509 of FIG. 1-5,and/or to implement phase modulator 1-601 of FIG. 1-6, and/or toimplement part of the amplitude modulators 1-603 of FIG. 1-6, and/or toimplement part of the amplitude modulators 1-205 of FIG. 1-2.

FIG. 6-1A is a top view illustrating schematically aNano-Opto-Electromechanical Systems (NOEMS) phase modulator, inaccordance with some non-limiting embodiments. NOEMS phase modulator6-100 includes input waveguide 6-102, output waveguide 6-104, inputtransition region 6-140, output transition region 6-150, suspendedmulti-slot optical structure 6-120, mechanical structures 6-130 and6-132, and mechanical drivers 6-160 and 6-162. NOEMS phase modulator6-100 may be fabricated using silicon photonic techniques. For example,NOEMS phase modulator 6-100 may be fabricated on a silicon substrate,such as a bulk silicon substrate or a silicon-on-insulator (SOI)substrate. In some embodiments, NOEMS phase modulator 6-100 may furtherinclude electronic circuitry configured to control the operations ofmechanical drivers 6-160 and 6-162. The electronic circuitry may befabricated on the same substrate hosting the components of FIG. 6-1A, oron a separate substrate. When disposed on a separate substrate, thesubstrates may be bonded to one another in a any suitable way, including3D-bonding, flip-chip bonding, wire bonding etc.

At least part of NOEMS phase modulator 6-100 is formed in a trench6-106. As will be described in detail further below, trenches of thetypes described herein may be formed by etching a portion of thecladding. In the example of FIG. 6-1A, trench 6-106 has a rectangularshape, though trenches of any other suitable shape may be used. In thisexample, trench 6-106 has four sidewalls. Sidewalls 6-112 and 6-114 arespaced from one another along the z-axis (referred to herein as thepropagation axis), and the other two sidewalls (not labeled in FIG.6-1A) are spaced from one another along the x-axis.

In some embodiments, the separation along the z-axis between sidewalls6-112 and 6-114 may be less than or equal to 50 μm, less than or equalto 30 μm, or less than or equal to 20 μm. Thus, the modulation region ofthis NOEMS phase modulator is significantly shorter than other types ofphase modulators, which require several hundreds of microns formodulating the phase of an optical signal. The relatively short lengthis enable by one or more of the following factors. First, havingmultiple slots improves coupling to the optical modulation region, whichin turn enables a reduction in the length of the transition region. Theimproved coupling may be the result of enhanced mode symmetry in themulti-slot structure. Second, decoupling the mechanical drivers from theoptical modulation region enables a greater modulation per unit length,and as a result a shorter modulation region.

During operation, an optical signal may be provided to input waveguide6-102. In one example, the optical signal may be a continuous wave (CW)signal. Phase modulation may take place in suspended multi-slot opticalstructure 6-120. A phase modulated optical signal may exit NOEMS phasemodulator 6-100 from output waveguide 6-104. Transition region 6-140 mayensure loss-free or nearly loss-free optical coupling between inputwaveguide 6-102 and suspended multi-slot optical structure 6-120.Similarly, transition region 6-150 may ensure loss-free or nearlyloss-free optical coupling between suspended multi-slot opticalstructure 6-120 and output waveguide 6-104. Transitions regions 6-140and 6-150 may include tapered waveguides in some embodiments, asdescribed in detail further below. As discussed above, the length of thetransitions regions may be shorter relative to other implementations.

The input optical signal may have any suitable wavelength, including butnot limited to a wavelength in the O-band, E-band, S-band, C-band orL-band. Alternatively, the wavelength may be in the 850 nm-band or inthe visible band. It should be appreciated that NOEMS phase modulator6-100 may be made of any suitable material, so long as the material istransparent or at least partially transparent at the wavelength ofinterest, and the refractive index of the core region is greater thanthe refractive index of the surrounding cladding. In some embodiments,NOEMS phase modulator 6-100 may be made of silicon. For example, inputwaveguide 6-102, output waveguide 6-104, input transition region 6-140,output transition region 6-150, suspended multi-slot optical structure6-120, and mechanical structures 6-130 and 6-132 may be made of silicon.Given silicon's relatively low optical bandgap (approximately 1.12 eV),silicon may be particularly suitable for use in connection with nearinfrared wavelengths. In another example, NOEMS phase modulator 6-100may be made of silicon nitride or diamond. Given silicon nitride's anddiamond's relatively high optical bandgaps (approximately 5 eV andapproximately 5.47 eV, respectively), these materials may beparticularly suitable for use in connection with visible wavelengths.However, other materials are also possible, including indium phosphide,gallium arsenide, and or any suitable III-V or II-VI alloy.

In some embodiments, input waveguide 6-102 and output waveguide 6-104may be sized to support a single mode at the wavelength of operation(though multi-mode waveguides can also be used). For example, if a NOEMSphase modulator is designed to operate at 1550 nm (though of course, notall embodiments are limited in this respect), input and outputwaveguides 6-102 and 6-104 may support a single mode at 1550 nm. In thisway, the mode confinement within the waveguide may be enhanced, thusreducing optical losses due to scattering and reflections. Waveguides6-102 and 6-104 may be rib waveguides (e.g., with rectangular crosssections) or may have any other suitable shape.

As described above, part of NOEMS phase modulator 6-100 may be formedwithin a trench 6-106, so that the waveguides in the modulation regionare surrounded by air and are free to move in space. The drawback ofincluding a trench is the formation of a cladding/air interface and anair/cladding interface along the propagation path. Thus, the inputoptical signal passes a cladding/air interface (in correspondence withsidewall 6-112) before reaching the region where modulation occurs andpasses an air/cladding interface (in correspondence with sidewall 6-114)after the modulation region. These interfaces may introduce reflectionlosses. In some embodiments, reflection losses may be reduced bypositioning transition region 6-140 inside, rather than outside, trench6-106 (as shown in FIG. 6-1A). In this way, the mode expansionassociated with the transition region takes place where the opticalsignal has already passed the cladding/air interface. In other words,the mode is tightly confined as it passes the cladding/air interface,but is expanded in the trench, using the transition region, for purposesof coupling to the suspended multi-slot structure 6-120. Similarly,transition region 6-150 may be formed inside trench 6-106, therebyspatially re-confining the mode before it reaches sidewall 6-114.

FIG. 6-1B illustrates suspended multi-slot optical structure 6-120 inadditional detail, in accordance with some non-limiting embodiments. Inthe example of FIG. 6-1B, multi-slot optical structure 6-120 includesthree waveguides (6-121, 6-122 and 6-123). Slot 6-124 separateswaveguide 6-121 from waveguide 6-122 and slot 6-125 separates waveguide6-122 from waveguide 6-123. The width of the slots (d₁ and d₂) may beless than the critical width (at the wavelength of operation) forforming slot modes, whereby a substantial portion of the mode energy(e.g., more than 40%, more than 50%, more than 60%, or more than 75%) iswithin the slots. For example, each of d₁ and d₂ may be equal to or lessthan 200 nm, equal to or less than 6-150 nm, or equal to or less than6-100 nm. The minimum width may be set by the photolithographicresolution.

FIG. 6-1C is a plot illustrating an example of an optical mode supportedby the waveguides 6-121, 6-122 and 6-123, in accordance with somenon-limiting embodiments. More specifically, the plot illustrates theamplitude of a mode (e.g., the electric field E_(x), E_(y) or E_(z), ormagnetic field H_(x), H_(y) or H_(z),). As illustrated, a substantialportion of the overall energy is confined within the slots, where themode exhibits peaks of amplitude. In some embodiments, there is moreoptical energy in one of the slots than in any one of the individualwaveguides. In some embodiments, there is more optical energy in one ofthe slots than in all the waveguides considered together. Outside theouter walls of the exterior waveguides, the mode energy decays (forexample exponentially).

Widths d₁ and d₂ may be equal to, or different than, one another. Thewidths of the slots and the waveguides may be constant along the z-axis(as in FIG. 6-1B) or may vary. In some embodiments, the widths ofwaveguides 6-121, 6-122 and 6-123 may be less than the width of inputwaveguide 6-102. In some embodiments, when the wavelength of operationis in the C-band, the widths of waveguides 6-121, 6-122 and 6-123 may bebetween 200 nm and 400 nm, between 250 nm and 350 nm, or within anyother suitable range, whether within or outside such ranges.

While the example of FIG. 6-1B illustrates suspended a multi-slotoptical structure 6-120 with three waveguides and two slots, any othersuitable number of waveguides and slots may be used. In other examples,a suspended multi-slot optical structure 6-120 may include fivewaveguides and four slots, seven waveguides and six slots, ninewaveguides and eight slots, etc. In some embodiments, the structureincludes an odd number of waveguides (and consequently, an even numberof slots) so that only symmetric modes are excited, while antisymmetricmodes remain unexcited. The inventors have appreciated that enhancingthe symmetry of the mode enhances coupling into the slotted structure,thus enabling a substantial reduction in the length of the transitionregion. However, implementations with even number of waveguides are alsopossible.

As will be described in detail further below, phase modulation occurs bycausing the exterior waveguides (6-121 and 6-123 in FIG. 6-1B) to moverelative to the center waveguide (6-122 in FIG. 6-1B) along the x-axis.When waveguide 6-121 moves in the x-axis relative to waveguide 6-122,the width of slot 6-124 varies, and the shape of the mode supported bythe structure varies accordingly. The result is a change in theeffective index of the mode supported by the structure, andconsequently, a phase modulation takes place. Motion of the exteriorwaveguides may be induced using mechanical structures 6-130 and 6-132.

An example of a mechanical structure 6-130 is illustrated in FIG. 6-1D,in accordance with some non-limiting embodiments. Mechanical structure6-132 (see FIG. 6-1A) may have a similar arrangement. In the example ofFIG. 6-1D, mechanical structure 6-130 includes beams 6-133, 6-134, 6-135and 6-136. Beam 6-133 connects mechanical driver 6-160 to beam 6-134.Beams 6-135 and 6-136 connect beam 6-134 to the exterior waveguide. Tolimit optical losses, beams 6-135 and 6-136 may be attached to theexterior waveguide in the transition regions 6-140 and 6-150,respectively, rather than in the modulation region (as shown in FIG.6-1E, which is discussed below). However, attaching beams 6-135 and6-136 to the exterior waveguide to the modulation region is alsopossible. Beams with different shapes, sizes and orientations may beused in alternative or in addition to those illustrated in FIG. 6-1D.

Mechanical structure 6-130 may transfer mechanical forces generated atmechanical driver 6-160 to waveguide 6-121, thereby causing waveguide6-121 to move relative to waveguide 6-122. Mechanical drivers 6-160 and6-162 may be implemented in any suitable way. In one example, themechanical drivers may include piezoelectric devices. In one example,the mechanical drivers may include conductive fingers. When a voltage isapplied between adjacent fingers, the fingers may experienceacceleration, thus imparting a mechanical force to the mechanicalstructures. In some embodiments, the mechanical drivers may be drivenwith an electrical signal having a pattern encoded thereon. In this way,modulation results in the pattern being imparted onto the phase of aninput optical signal.

It should be appreciated that, because the waveguides of suspendedmulti-slot optical structure 6-120 are driven using external mechanicaldrivers, rather than being directly supplied with electrical signals asin certain conventional phase modulators, the conductivity of thewaveguides can be relaxed, thus reducing free carrier absorption loss,and consequently, dynamic loss. This is different than some conventionalphase modulators, where the waveguide itself is doped to act as a heateror a carrier accumulation region. In some embodiments, waveguides 6-121,6-122 and 6-123 may be made of an undoped, or low-doped, semiconductormaterial (e.g., undoped silicon or silicon with a doping concentrationless than 10¹⁴ cm³). In some embodiments, the resistivity of thematerial forming the waveguides may be greater than 1300 Ωcm.

FIG. 6-1E illustrates an example of a transition region 6-140, inaccordance with some non-limiting embodiments. In this implementation,waveguide 6-122 is contiguous to (e.g., is the continuation of) inputwaveguide 6-102. As shown, waveguide 6-122 is tapered in the transitionregion such that its width reduces as it approaches the suspendedmulti-slot optical structure 6-120. By contrast, waveguides 6-121 and6-123 are tapered in the transition region such that their widthsincrease as they depart from suspended multi-slot optical structure6-120. The tapered waveguides may allow adiabatic coupling between themode of input waveguide 6-102 and the mode of suspended multi-slotoptical structure 6-120, thereby limiting coupling losses. A similararrangement may be used for transition region 6-150. Due to the enhancedsymmetry of the mode supported by the multi-slot structure, transitionregions 6-140 and 6-150 are significantly shorter than otherimplementations. In some embodiments, the transition regions may be asshort as 10 μm or less, or 5 μm or less, though other values are alsopossible.

FIG. 6-2 is a cross sectional view of a NOEMS phase modulator 6-100taken in a yz-plane passing through waveguide 6-122 (see plane 6-190 inFIG. 6-1B), in accordance with some non-limiting embodiments. Inputwaveguide 6-102 and output waveguide 6-104 are surrounded by a claddingmade of a material (e.g., silicon oxide) with a refractive index lowerthan the refractive index of the core material. Lower cladding 6-202 isbetween the waveguide and the underlying substrate 6-201. Upper cladding6-206 is formed over the waveguide.

To enable free motion of the waveguides of the suspended multi-slotoptical structure 6-120, a trench 6-106 is formed through part of uppercladding 6-206. In some embodiments, a portion of the lower cladding6-202 is removed under the suspended multi-slot optical structure 6-120,thus forming an undercut 6-204. As a result, waveguides 6-121, 6-122 and6-123 are suspended in air and are free to move responsive to mechanicalforces. A cladding/air interface exists at trench sidewall 6-112 and anair/cladding interface exists at trench sidewall 6-114. The sidewallsmay be substantially vertical, for example if the trench is formed byreaction ion etching (RIE), or may alternatively be angled. Undercut6-204 may have curved sidewalls, as illustrated in FIG. 6-2, if anisotropic etch is used, or may alternatively be substantially vertical.In some embodiments, trench 6-106 and undercut 6-204 may be formed aspart of the same etch, while in other embodiments, they be formed usingseparate etches.

FIG. 6-3 is a cross sectional view of a NOEMS phase modulator 6-100taken in a xy-plane passing through waveguides 6-121, 6-122 and 6-123(see plane 6-191 in FIG. 6-1B), in accordance with some non-limitingembodiments. FIG. 6-3 shows that waveguides 6-121, 6-122 and 6-123 andbeams 6-134, are co-planar (at least in this example), and are suspendedin air above substrate 6-201. As further illustrated in this figure,waveguides 6-121, 6-122 and 6-123 do not contact lower cladding 6-202 atthis cross section. When mechanical drivers 6-160 and 6-162 areactuated, beams 6-134 and waveguides 6-121 and 6-123 oscillate along thex-axis, thus varying the widths of the slots 6-124 and 6-125. An exampleof an oscillatory motion of waveguides 6-121 and 6-123 is illustrated,collectively, in FIGS. 6-4A through 6-4C, in accordance with somenon-limiting embodiments. FIG. 6-4A illustrates a case in which nomechanical force is applied. As a result, the widths of the slots areunperturbed. In FIG. 6-4B, a pair of forces is applied such that bothwaveguides 6-121 and 6-123 move towards waveguide 6-122, as illustratedby the arrows. As a result, the widths of the slots are reduced. In FIG.6-4C, a pair of forces is applied such that both waveguides 6-121 and6-123 move away from waveguide 6-122, also illustrated by the arrows. Asa result, the widths of the slots are increased. In some embodiments,the forces may be applied in a periodic fashion, and/or following thepattern of the driving electrical signals. In some embodiments, theforces may be applied to waveguides 6-121 and 6-123 differentially, suchthat the same intensity is applied to both waveguides but with oppositesigns.

FIG. 6-5 is a plot illustrating how the effective refractive index(Neff) of the mode propagating in the suspended multi-slot opticalstructure 6-120 varies as a function of width d₁ (the width of the slotbetween waveguides 6-121 and 6-122), in accordance with somenon-limiting embodiments. A similar response may be plotted as afunction of d₂. The effective index variation is caused by the factthat, as the separation between the waveguides varies under the effectof an applied mechanical force, the shape of the mode deviates relativeto the one illustrated in FIG. 6-1C. As the width varies over time, sodoes the mode effective index, and consequently, the phase of the mode.

FIG. 6-6 is a flowchart illustrating an example of a method forfabricating a NOEMS phase modulator, in accordance with somenon-limiting embodiments. It should be appreciated that the steps of themethod described below may be performed in any suitable order, asfabrication processes are not limited to the specific order illustratedin FIG. 6-6.

Fabrication method 6-600 begins at step 6-602, in which a chip isobtained. In some embodiments, the chip may be a silicon-on-insulatorchip, or a bulk silicon chip. The chip may have a substrate and any ofthe following layers: a lower cladding layer, a semiconductor layer andan upper cladding layer. The lower cladding layer may comprise siliconoxide in some embodiments. The semiconductor layer may comprisessilicon, silicon nitride and/or doped silicon oxide in some embodiments.The upper cladding layer may comprise the same material forming thelower cladding layer, or a different material. FIG. 6-3. Illustrates anexamples of a substrate (substrate 6-201) having a lower cladding layer(cladding 6-202), a semiconductor layer (the layer of waveguides 6-121,6-122 and 6-123) and an upper cladding layer (cladding 6-206). It shouldbe appreciated that any of the layers identified above may already bepresent on the chip when the chip arrives at the fabrication facility(where the NOEMS phase modulator is fabricated), or may be formed at thefacility as part of the fabrication process.

At step 6-604, the semiconductor layer is patterned form a multi-slotoptical structure having first and second slots (or any other number ofslots greater than two). In the example of FIG. 6-3, waveguides 6-121,6-122 and 6-123 may be formed at step 6-604. Patterning thesemiconductor layer may involve deposition of a photoresist layer, aphotolithographic exposure and etching through the semiconductor layer.In some embodiments, any one of mechanical structures 6-130 and 6-132,mechanical drivers 6-160 and 6-162, waveguides 6-102 and 6-104 andtransition regions 6-140 and 6-142 (see FIG. 6-1A) are fabricated aspart of the same photolithographic exposure, though not all embodimentsare limited in this respect as one or more separate photolithographicexposures may be used. In some embodiments, at step 6-604, mechanicaldrivers 6-160 may be doped, for example using ion implantation. In someembodiments, the multi-slot optical structure may remain undoped.

At step 6-606, a trench may be formed through the upper cladding layer.An example of a trench (trench 6-106) is illustrated at FIG. 6-3. Thetrench may be formed, for example, using a dry etch such as a reactiveion etch. However, wet etches may alternatively or additionally be used.Formation of the trench may involve removal of a portion of the uppercladding layer in a region above the multi-slot optical structure formedat step 6-604. As a result, the multi-slot optical structure may beexposed, partially or entirely, to air.

At step 6-608, an undercut may be formed in the lower cladding layer. Anexample of an undercut (undercut 6-204) is illustrated at FIG. 6-3. Theundercut may be formed, for example, using a wet etch, though dry etchesmay alternatively or additionally be used. Formation of the undercut mayinvolve removal of a portion of the lower cladding layer in a regionunder the multi-slot optical structure. As a result, at least part ofthe multi-slot optical structure may be suspended over air.

Having thus described several aspects and embodiments of the technologyof this application, it is to be appreciated that various alterations,modifications, and improvements will readily occur to those of ordinaryskill in the art. Such alterations, modifications, and improvements areintended to be within the spirit and scope of the technology describedin the application. It is, therefore, to be understood that theforegoing embodiments are presented by way of example only and that,within the scope of the appended claims and equivalents thereto,inventive embodiments may be practiced otherwise than as specificallydescribed. In addition, any combination of two or more features,systems, articles, materials, and/or methods described herein, if suchfeatures, systems, articles, materials, and/or methods are not mutuallyinconsistent, is included within the scope of the present disclosure.

Also, as described, some aspects may be embodied as one or more methods.The acts performed as part of the method may be ordered in any suitableway. Accordingly, embodiments may be constructed in which acts areperformed in an order different than illustrated, which may includeperforming some acts simultaneously, even though shown as sequentialacts in illustrative embodiments.

All definitions, as defined and used herein, should be understood tocontrol over dictionary definitions, definitions in documentsincorporated by reference, and/or ordinary meanings of the definedterms.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified.

The terms “approximately” and “about” may be used to mean within ±20% ofa target value in some embodiments, within ±10% of a target value insome embodiments, within ±5% of a target value in some embodiments, andyet within ±2% of a target value in some embodiments. The terms“approximately” and “about” may include the target value.

What is claimed is:
 1. A photonic computing system comprising: a lightsource configured to generate a plurality of input optical signalshaving mutually different wavelengths; a plurality of optical encodersconfigured to encode the plurality of input optical signals usingrespective input numeric values; a first wavelength division multiplexer(WDM) configured to spatially combine the plurality of encoded inputoptical signals into an input waveguide; a photonic multiplier coupledto the input waveguide and configured to generate a plurality of outputoptical signals, each output optical signal being encoded with arespective output numeric value that is representative of a product of arespective input numeric value with a common weight numeric value; and asecond WDM coupled to the photonic multiplier and configured tospatially separate the plurality of output optical signals.
 2. Thephotonic computing system of claim 1, wherein the light source comprisesan array of lasers, a first laser of the array being configured togenerate a first input optical signal having a first wavelength and asecond laser of the array being configured to generate a second inputoptical signal having a second wavelength different from the firstwavelength.
 3. The photonic computing system of claim 1, furthercomprising a plurality of optical receivers configured to generate aplurality of output analog signals using the plurality of output opticalsignals.
 4. The photonic computing system of claim 3, wherein theplurality of optical receivers comprise coherent optical receiversconfigured to generate the plurality of output analog signals using theplurality of output optical signals and a plurality of optical referencesignals derived from the light source.
 5. The photonic computing systemof claim 3, further comprising a controller configured to perform afirst iteration comprising: receiving an input bit string, generating,using the input bit string, input analog signals representing theplurality of input numeric values, and providing the input analogsignals to the plurality of optical encoders, setting the photonicmultiplier based on the weight numeric value; receiving the outputanalog signals from the plurality of optical receivers, and generating,using the output numeric values, an output bit string.
 6. The photoniccomputing system of claim 5, wherein the controller is furtherconfigured to perform a second iteration comprising: generating, usingthe output bit string, further input analog signals representing afurther plurality of input numeric values, and providing the furtherinput analog signals to the plurality of optical encoders.
 7. Thephotonic computing system of claim 1, wherein the input optical signalshave wavelengths in the O-band, C-band or L-band.
 8. The photoniccomputing system of claim 1, wherein the plurality of optical encoderscomprise optical amplitude modulators configured to encode amplitudes ofthe plurality of input optical signals using respective input numericvalues.
 9. The photonic computing system of claim 1, wherein theplurality of optical encoders comprise optical phase modulatorsconfigured to encode phases of the plurality of input optical signalsusing respective input numeric values.
 10. A photonic computing systemcomprising: a light source configured to generate a plurality of inputoptical signals having mutually different wavelengths; a firstwavelength division multiplexer (WDM) configured to spatially combinethe plurality of input optical signals into an input waveguide; aphotonic processor coupled to the input waveguide and configured togenerate a plurality of output optical signals by performing amatrix-matrix multiplication or matrix-vector multiplication using theplurality of input optical signals; a second WDM coupled to the photonicprocessor and configured to spatially separate the plurality of outputoptical signals; a plurality of optical receivers coupled to the secondWDM and configured to generate a plurality of output analog signalsusing the plurality of output optical signals, wherein each of theplurality of optical receivers comprises at least two photodetectors;and a plurality of signal combiners each being coupled to the at leasttwo photodetectors of a respective optical receiver.
 11. The photoniccomputing system of claim 10, wherein the plurality of optical receiversare configured to generate the plurality of output analog signals bycombining the plurality of output optical signals with a plurality ofoptical reference signals, and wherein the optical reference signals arederived from the light source.
 12. The photonic computing system ofclaim 10, wherein the light source comprises an array of lasers, a firstlaser of the array being configured to generate a first input opticalsignal having a first wavelength and a second laser of the array beingconfigured to generate a second input optical signal having a secondwavelength different from the first wavelength.
 13. The photoniccomputing system of claim 10, further comprising a controller configuredto perform a first iteration comprising: receiving an input bit string,generating, using the input bit string, input analog signalsrepresenting a plurality of input numeric values, and providing theinput analog signals to a plurality of optical encoders, setting thephotonic processor based on a weight numeric value; receiving the outputanalog signals from the plurality of optical receiver, and generating,using the output numeric values, an output bit string.
 14. The photoniccomputing system of claim 13, wherein the controller is furtherconfigured to perform a second iteration comprising: generating, usingthe output bit string, further input analog signals representing afurther plurality of input numeric values, and providing the furtherinput analog signals to the plurality of optical encoders.
 15. Thephotonic computing system of claim 13, wherein the plurality of opticalencoders comprise optical amplitude modulators configured to encodeamplitudes of the plurality of input optical signals using respectiveinput numeric values.
 16. The photonic computing system of claim 13,wherein the plurality of optical encoders comprise optical phasemodulators configured to encode phases of the plurality of input opticalsignals using respective input numeric values.
 17. The photoniccomputing system of claim 10, wherein the plurality of signal combinerscomprises a plurality of subtraction circuits.
 18. A photonic computingsystem comprising: a plurality of vertical-cavity surface emittinglasers (VCSELs) configured to generate a plurality of input opticalsignals that have mutually different wavelengths and that are encodedwith respective input numeric values; a first wavelength divisionmultiplexer (WDM) configured to spatially combine the plurality ofencoded input optical signals into an input waveguide; a photonicmultiplier coupled to the input waveguide and configured to generate aplurality of output optical signals, each output optical signal beingencoded with a respective output numeric value that is representative ofa product of a respective input numeric value with a common weightnumeric value; and a second WDM coupled to the photonic multiplier andconfigured to spatially separate the plurality of output opticalsignals.
 19. The photonic computing system of claim 18, furthercomprising a plurality of optical receivers configured to generate aplurality of output analog signals using the plurality of output opticalsignals.
 20. The photonic computing system of claim 19, wherein theplurality of optical receivers comprise coherent optical receiversconfigured to generate the plurality of output analog signals using theplurality of output optical signals and a plurality of optical referencesignals derived from the VCSELs.